{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Comparison of predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- See preprocessing of gene expression and prediction were done in CaDRReS2/pipeline/* and 03_*\n",
    "- Convert predicted delta to cv to cell death percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.042873Z",
     "start_time": "2020-11-17T13:31:50.034340Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn import metrics\n",
    "from collections import Counter\n",
    "\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style('ticks')\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pd.set_option('precision', 2)\n",
    "np.set_printoptions(suppress=True)\n",
    "from IPython.display import HTML, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.048505Z",
     "start_time": "2020-11-17T13:31:50.045012Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi']= 120\n",
    "mpl.rc(\"savefig\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Compare between cell line prediction and cell type-specific prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.053424Z",
     "start_time": "2020-11-17T13:31:50.050700Z"
    }
   },
   "outputs": [],
   "source": [
    "dosage_shifted = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for experimental validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.058376Z",
     "start_time": "2020-11-17T13:31:50.055365Z"
    }
   },
   "outputs": [],
   "source": [
    "dosage_used = '3 fold' # All for HN, '9 fold' '3 fold' 'Median IC50' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for calculating % cell death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.063264Z",
     "start_time": "2020-11-17T13:31:50.060211Z"
    }
   },
   "outputs": [],
   "source": [
    "# log2_median_ic50, log2_median_ic50_9f, log2_median_ic50_hn, log2_median_ic50_9f_hn, log2_median_ic50_3f_hn, log2_max_conc\n",
    "dosage_ref = 'log2_median_ic50_hn' # log2_median_ic50_hn | log2_median_ic50_3f_hn\n",
    "model_name = 'RWEN'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.067841Z",
     "start_time": "2020-11-17T13:31:50.065040Z"
    }
   },
   "outputs": [],
   "source": [
    "current_dir = '../result/HN_model/cell_TPM/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.081970Z",
     "start_time": "2020-11-17T13:31:50.069665Z"
    }
   },
   "outputs": [],
   "source": [
    "if dosage_shifted:\n",
    "    pred_single_df = pd.read_csv(current_dir + 'pred_drug_kill_{}_{}_shifted.csv'.format(dosage_ref, model_name))\n",
    "    pred_combi_df = pd.read_csv(current_dir + 'pred_combi_kill_{}_{}_shifted.csv'.format(dosage_ref, model_name))\n",
    "else:\n",
    "    pred_single_df = pd.read_csv(current_dir + 'pred_drug_kill_{}_{}.csv'.format(dosage_ref, model_name))\n",
    "    pred_combi_df = pd.read_csv(current_dir + 'pred_combi_kill_{}_{}.csv'.format(dosage_ref, model_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read experimental data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.212322Z",
     "start_time": "2020-11-17T13:31:50.084631Z"
    },
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File name</th>\n",
       "      <th>Dosage</th>\n",
       "      <th>Drug</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>1</td>\n",
       "      <td>56.98</td>\n",
       "      <td>64.74</td>\n",
       "      <td>40.52</td>\n",
       "      <td>26.77</td>\n",
       "      <td>97.41</td>\n",
       "      <td>67.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>2</td>\n",
       "      <td>58.10</td>\n",
       "      <td>64.20</td>\n",
       "      <td>38.30</td>\n",
       "      <td>25.42</td>\n",
       "      <td>96.90</td>\n",
       "      <td>68.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>3</td>\n",
       "      <td>53.09</td>\n",
       "      <td>59.52</td>\n",
       "      <td>35.23</td>\n",
       "      <td>24.12</td>\n",
       "      <td>93.86</td>\n",
       "      <td>66.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1</td>\n",
       "      <td>81.52</td>\n",
       "      <td>71.17</td>\n",
       "      <td>84.23</td>\n",
       "      <td>86.19</td>\n",
       "      <td>88.00</td>\n",
       "      <td>67.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>2</td>\n",
       "      <td>80.69</td>\n",
       "      <td>67.84</td>\n",
       "      <td>78.09</td>\n",
       "      <td>81.28</td>\n",
       "      <td>86.22</td>\n",
       "      <td>70.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               File name  Dosage       Drug  Replicate  HN120  \\\n",
       "0  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          1  56.98   \n",
       "1  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          2  58.10   \n",
       "2  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          3  53.09   \n",
       "6  validation_triplicate_2019_09_13.xlsx  3 fold  Docetaxel          1  81.52   \n",
       "7  validation_triplicate_2019_09_13.xlsx  3 fold  Docetaxel          2  80.69   \n",
       "\n",
       "   HN137  HN148  HN159  HN160  HN182  \n",
       "0  64.74  40.52  26.77  97.41  67.09  \n",
       "1  64.20  38.30  25.42  96.90  68.63  \n",
       "2  59.52  35.23  24.12  93.86  66.52  \n",
       "6  71.17  84.23  86.19  88.00  67.97  \n",
       "7  67.84  78.09  81.28  86.22  70.59  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Combined replicate #####\n",
    "\n",
    "validation_fname_list = ['../result/validation/validation_triplicate_2019_09_13.xlsx', '../result/validation/validation_replicates_2019_06_24.xlsx']\n",
    "\n",
    "cv_single_df_list = []\n",
    "cv_combi_df_list = []\n",
    "\n",
    "for validation_fname in validation_fname_list:\n",
    "\n",
    "    cv_single_df = pd.read_excel(validation_fname, sheet_name='cv_single', index_col=[0,1,2])\n",
    "    cv_single_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "    cv_single_df = cv_single_df.reset_index().groupby(['File name', 'Dosage', 'Drug', 'Replicate']).median().reset_index() # just to rearrange columns\n",
    "\n",
    "    cv_combi_df = pd.read_excel(validation_fname, sheet_name='cv_combi', index_col=[0,1,2])\n",
    "    cv_combi_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "    cv_combi_df = cv_combi_df.reset_index().groupby(['File name', 'Dosage', 'Drug', 'Replicate']).median().reset_index()\n",
    "    \n",
    "    cv_single_df_list += [cv_single_df]\n",
    "    cv_combi_df_list += [cv_combi_df]\n",
    "\n",
    "cv_df = pd.concat(cv_single_df_list + cv_combi_df_list, axis=0)\n",
    "cv_df = cv_df[~cv_df['Drug'].isin(['DMSO', 'Staurosporin'])]\n",
    "cv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.217847Z",
     "start_time": "2020-11-17T13:31:50.214794Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "##### Not combined replicate #####\n",
    "\n",
    "# validation_fname_list = ['../result/validation/validation_triplicate_2019_09_13.xlsx', '../result/validation/validation_replicates_2019_06_24.xlsx']\n",
    "\n",
    "# cv_single_df_list = []\n",
    "# cv_combi_df_list = []\n",
    "\n",
    "# for validation_fname in validation_fname_list:\n",
    "\n",
    "#     cv_single_df = pd.read_excel(validation_fname, sheet_name='cv_single', index_col=[0,1,2])\n",
    "#     cv_single_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "#     cv_single_df = cv_single_df.reset_index()\n",
    "\n",
    "#     cv_combi_df = pd.read_excel(validation_fname, sheet_name='cv_combi', index_col=[0,1,2])\n",
    "#     cv_combi_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "#     cv_combi_df = cv_combi_df.reset_index()\n",
    "    \n",
    "#     cv_single_df_list += [cv_single_df]\n",
    "#     cv_combi_df_list += [cv_combi_df]\n",
    "\n",
    "# cv_df = pd.concat(cv_single_df_list + cv_combi_df_list, axis=0)\n",
    "# cv_df = cv_df[~cv_df['Drug'].isin(['DMSO', 'Staurosporin'])]\n",
    "# cv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.226146Z",
     "start_time": "2020-11-17T13:31:50.219848Z"
    }
   },
   "outputs": [],
   "source": [
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN159', 'HN160', 'HN182']\n",
    "patient_list = ['HN120', 'HN137', 'HN148', 'HN159', 'HN160']\n",
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN160']\n",
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN159']\n",
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN160']\n",
    "\n",
    "# single_drug_id_list = [1032, 1007, 133, 201, 1010] + [182, 301, 302] + [1012]\n",
    "# single_drug_list = ['Afatinib', 'Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib'] + ['Obatoclax Mesylate', 'PHA-793887', 'PI-103'] + ['Vorinostat']\n",
    "# combi_drug_list = ['Docetaxel|Afatinib', 'Docetaxel|Epothilone B', 'Docetaxel|Gefitinib', 'Epothilone B|Afatinib', 'Gefitinib|Afatinib', 'Gefitinib|Epothilone B'] + ['Afatinib|Obatoclax Mesylate', 'Epothilone B|PI-103'] + ['Doxorubicin|Vorinostat']\n",
    "\n",
    "single_drug_id_list = [1007, 133, 201, 1010] + [182, 301, 302] + [1012]\n",
    "single_drug_list = ['Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib'] + ['Obatoclax Mesylate', 'PHA-793887', 'PI-103'] + ['Vorinostat']\n",
    "combi_drug_list = ['Docetaxel|Epothilone B', 'Docetaxel|Gefitinib', 'Gefitinib|Epothilone B'] + ['Epothilone B|PI-103'] + ['Doxorubicin|Vorinostat']\n",
    "\n",
    "# single_drug_id_list = [1032, 1007, 133, 201, 1010] + [182, 301, 302]\n",
    "# single_drug_list = ['Afatinib', 'Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib'] + ['Obatoclax Mesylate', 'PHA-793887', 'PI-103']\n",
    "# combi_drug_list = ['Docetaxel|Afatinib', 'Docetaxel|Epothilone B', 'Docetaxel|Gefitinib', 'Epothilone B|Afatinib', 'Gefitinib|Afatinib', 'Gefitinib|Epothilone B'] + ['Afatinib|Obatoclax Mesylate', 'Epothilone B|PI-103']\n",
    "\n",
    "# single_drug_id_list = [1007, 133, 1010, 182, 301, 302]\n",
    "# single_drug_list = ['Docetaxel', 'Doxorubicin', 'Gefitinib', 'Obatoclax Mesylate', 'PHA-793887', 'PI-103']\n",
    "# combi_drug_list = ['Docetaxel|Gefitinib']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.238128Z",
     "start_time": "2020-11-17T13:31:50.228180Z"
    }
   },
   "outputs": [],
   "source": [
    "# read reference dosages file\n",
    "dosage_df = pd.read_csv('../preprocessed_data/GDSC/hn_drug_stat.csv', index_col=0)\n",
    "dosage_df = dosage_df.loc[single_drug_id_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.242458Z",
     "start_time": "2020-11-17T13:31:50.239804Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "##### Check consistency across triplicate #####\n",
    "\n",
    "# validation_fname = validation_fname_list[0]\n",
    "\n",
    "# cv_new_df = pd.concat([pd.read_excel(validation_fname, sheet_name='cv_single', index_col=[0,1,2]), pd.read_excel(validation_fname, sheet_name='cv_combi', index_col=[0,1,2])], axis=0).reset_index()\n",
    "# cv_new_df = cv_new_df[~cv_new_df['Drug'].isin(['DMSO', 'Staurosporin'])]\n",
    "# cv_new_df = cv_new_df.set_index(['Drug', 'Dosage', 'Replicate']).stack().reset_index()\n",
    "# cv_new_df.columns = ['Drug', 'Dosage', 'Replicate', 'Patient', 'Viability']\n",
    "# cv_new_df = cv_new_df.groupby(['Dosage', 'Drug', 'Patient', 'Replicate']).sum().unstack()\n",
    "\n",
    "# sns.pairplot(cv_new_df, plot_kws={'s':10, 'alpha':0.75, 'linewidth':0}, diag_kws={'bins':20}, aspect=1.15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Preprocess predicted and observed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.254731Z",
     "start_time": "2020-11-17T13:31:50.244455Z"
    }
   },
   "outputs": [],
   "source": [
    "obs_kill_df = 100 - cv_df.set_index(['Dosage', 'Drug', 'File name', 'Replicate']).astype(float)\n",
    "obs_kill_df = obs_kill_df.loc[dosage_used]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.269241Z",
     "start_time": "2020-11-17T13:31:50.256284Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Afatinib</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>47.25</td>\n",
       "      <td>34.18</td>\n",
       "      <td>55.59</td>\n",
       "      <td>73.87</td>\n",
       "      <td>3.39</td>\n",
       "      <td>27.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.07</td>\n",
       "      <td>28.62</td>\n",
       "      <td>58.39</td>\n",
       "      <td>73.48</td>\n",
       "      <td>5.57</td>\n",
       "      <td>30.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.24</td>\n",
       "      <td>31.68</td>\n",
       "      <td>57.19</td>\n",
       "      <td>74.51</td>\n",
       "      <td>4.97</td>\n",
       "      <td>30.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Docetaxel</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>49.10</td>\n",
       "      <td>61.99</td>\n",
       "      <td>31.87</td>\n",
       "      <td>35.45</td>\n",
       "      <td>26.92</td>\n",
       "      <td>56.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45.15</td>\n",
       "      <td>58.86</td>\n",
       "      <td>36.26</td>\n",
       "      <td>29.41</td>\n",
       "      <td>26.08</td>\n",
       "      <td>54.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.05</td>\n",
       "      <td>58.84</td>\n",
       "      <td>32.00</td>\n",
       "      <td>32.61</td>\n",
       "      <td>25.09</td>\n",
       "      <td>58.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Doxorubicin</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>67.53</td>\n",
       "      <td>63.16</td>\n",
       "      <td>50.80</td>\n",
       "      <td>32.72</td>\n",
       "      <td>24.21</td>\n",
       "      <td>51.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64.43</td>\n",
       "      <td>62.38</td>\n",
       "      <td>54.29</td>\n",
       "      <td>29.53</td>\n",
       "      <td>23.48</td>\n",
       "      <td>52.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67.12</td>\n",
       "      <td>61.73</td>\n",
       "      <td>53.64</td>\n",
       "      <td>34.09</td>\n",
       "      <td>23.81</td>\n",
       "      <td>54.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B</th>\n",
       "      <th>validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>55.98</td>\n",
       "      <td>71.19</td>\n",
       "      <td>36.99</td>\n",
       "      <td>43.62</td>\n",
       "      <td>20.77</td>\n",
       "      <td>78.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              HN120  HN137  \\\n",
       "Drug         File name                             Replicate                 \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1          47.25  34.18   \n",
       "                                                   2          44.07  28.62   \n",
       "                                                   3          48.24  31.68   \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          49.10  61.99   \n",
       "                                                   2          45.15  58.86   \n",
       "                                                   3          50.05  58.84   \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          67.53  63.16   \n",
       "                                                   2          64.43  62.38   \n",
       "                                                   3          67.12  61.73   \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          55.98  71.19   \n",
       "\n",
       "                                                              HN148  HN159  \\\n",
       "Drug         File name                             Replicate                 \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1          55.59  73.87   \n",
       "                                                   2          58.39  73.48   \n",
       "                                                   3          57.19  74.51   \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          31.87  35.45   \n",
       "                                                   2          36.26  29.41   \n",
       "                                                   3          32.00  32.61   \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          50.80  32.72   \n",
       "                                                   2          54.29  29.53   \n",
       "                                                   3          53.64  34.09   \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          36.99  43.62   \n",
       "\n",
       "                                                              HN160  HN182  \n",
       "Drug         File name                             Replicate                \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1           3.39  27.10  \n",
       "                                                   2           5.57  30.12  \n",
       "                                                   3           4.97  30.80  \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          26.92  56.40  \n",
       "                                                   2          26.08  54.51  \n",
       "                                                   3          25.09  58.38  \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          24.21  51.81  \n",
       "                                                   2          23.48  52.38  \n",
       "                                                   3          23.81  54.06  \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          20.77  78.44  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_kill_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.293343Z",
     "start_time": "2020-11-17T13:31:50.271299Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_single_df = pred_single_df[pred_single_df['drug_id'].isin(single_drug_id_list)]\n",
    "\n",
    "pred_combi_df = pred_combi_df[pred_combi_df['drug_id_A'].isin(single_drug_id_list) & pred_combi_df['drug_id_B'].isin(single_drug_id_list)]\n",
    "\n",
    "pred_combi_df.loc[:, 'Combi Name 1'] = pred_combi_df['drug_name_A'].values + '|' + pred_combi_df['drug_name_B'].values\n",
    "pred_combi_df.loc[:, 'Combi Name 2'] = pred_combi_df['drug_name_B'].values + '|' + pred_combi_df['drug_name_A'].values\n",
    "\n",
    "temp1 = pred_combi_df['Combi Name 1'][pred_combi_df['Combi Name 1'].isin(combi_drug_list)]\n",
    "temp2 = pred_combi_df['Combi Name 2'][pred_combi_df['Combi Name 2'].isin(combi_drug_list)]\n",
    "combi_name = pd.concat([temp1, temp2]).values\n",
    "\n",
    "pred_combi_df = pd.concat([pred_combi_df[pred_combi_df['Combi Name 1'].isin(combi_drug_list)], pred_combi_df[pred_combi_df['Combi Name 2'].isin(combi_drug_list)]])\n",
    "pred_combi_df.loc[:, 'Combi Name'] = combi_name\n",
    "pred_combi_df = pred_combi_df[pred_combi_df['Combi Name'].isin(combi_drug_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.303976Z",
     "start_time": "2020-11-17T13:31:50.294981Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>drug_id</th>\n",
       "      <th>delta</th>\n",
       "      <th>kill</th>\n",
       "      <th>drug_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>56.82</td>\n",
       "      <td>Docetaxel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HN120</td>\n",
       "      <td>133</td>\n",
       "      <td>-3.11</td>\n",
       "      <td>87.30</td>\n",
       "      <td>Doxorubicin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HN120</td>\n",
       "      <td>201</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>76.85</td>\n",
       "      <td>Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1010</td>\n",
       "      <td>1.48</td>\n",
       "      <td>33.41</td>\n",
       "      <td>Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HN120</td>\n",
       "      <td>182</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>75.64</td>\n",
       "      <td>Obatoclax Mesylate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  patient  drug_id  delta   kill           drug_name\n",
       "0   HN120     1007  -0.46  56.82           Docetaxel\n",
       "1   HN120      133  -3.11  87.30         Doxorubicin\n",
       "2   HN120      201  -2.13  76.85        Epothilone B\n",
       "3   HN120     1010   1.48  33.41           Gefitinib\n",
       "4   HN120      182  -1.97  75.64  Obatoclax Mesylate"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_single_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.323695Z",
     "start_time": "2020-11-17T13:31:50.306012Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>drug_id_A</th>\n",
       "      <th>drug_name_A</th>\n",
       "      <th>drug_id_B</th>\n",
       "      <th>drug_name_B</th>\n",
       "      <th>cluster_p</th>\n",
       "      <th>cluster_kill_A</th>\n",
       "      <th>cluster_kill_B</th>\n",
       "      <th>kill_A</th>\n",
       "      <th>kill_B</th>\n",
       "      <th>kill_C</th>\n",
       "      <th>improve</th>\n",
       "      <th>improve_p</th>\n",
       "      <th>sum_kill_dif</th>\n",
       "      <th>Combi Name 1</th>\n",
       "      <th>Combi Name 2</th>\n",
       "      <th>Combi Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1</td>\n",
       "      <td>56.82</td>\n",
       "      <td>76.85</td>\n",
       "      <td>56.82</td>\n",
       "      <td>76.85</td>\n",
       "      <td>90.01</td>\n",
       "      <td>13.15</td>\n",
       "      <td>0.17</td>\n",
       "      <td>20.04</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>56.82</td>\n",
       "      <td>33.41</td>\n",
       "      <td>56.82</td>\n",
       "      <td>33.41</td>\n",
       "      <td>71.25</td>\n",
       "      <td>14.43</td>\n",
       "      <td>0.25</td>\n",
       "      <td>23.41</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HN120</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>1</td>\n",
       "      <td>87.30</td>\n",
       "      <td>69.75</td>\n",
       "      <td>87.30</td>\n",
       "      <td>69.75</td>\n",
       "      <td>96.16</td>\n",
       "      <td>8.86</td>\n",
       "      <td>0.10</td>\n",
       "      <td>17.54</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HN120</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>1</td>\n",
       "      <td>76.85</td>\n",
       "      <td>96.18</td>\n",
       "      <td>76.85</td>\n",
       "      <td>96.18</td>\n",
       "      <td>99.12</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0.03</td>\n",
       "      <td>19.33</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>HN137</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1</td>\n",
       "      <td>53.62</td>\n",
       "      <td>80.92</td>\n",
       "      <td>53.62</td>\n",
       "      <td>80.92</td>\n",
       "      <td>91.15</td>\n",
       "      <td>10.23</td>\n",
       "      <td>0.13</td>\n",
       "      <td>27.30</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient  drug_id_A   drug_name_A  drug_id_B   drug_name_B  cluster_p  \\\n",
       "1    HN120       1007     Docetaxel        201  Epothilone B          1   \n",
       "2    HN120       1007     Docetaxel       1010     Gefitinib          1   \n",
       "12   HN120        133   Doxorubicin       1012    Vorinostat          1   \n",
       "16   HN120        201  Epothilone B        302        PI-103          1   \n",
       "29   HN137       1007     Docetaxel        201  Epothilone B          1   \n",
       "\n",
       "    cluster_kill_A  cluster_kill_B  kill_A  kill_B  kill_C  improve  \\\n",
       "1            56.82           76.85   56.82   76.85   90.01    13.15   \n",
       "2            56.82           33.41   56.82   33.41   71.25    14.43   \n",
       "12           87.30           69.75   87.30   69.75   96.16     8.86   \n",
       "16           76.85           96.18   76.85   96.18   99.12     2.93   \n",
       "29           53.62           80.92   53.62   80.92   91.15    10.23   \n",
       "\n",
       "    improve_p  sum_kill_dif            Combi Name 1            Combi Name 2  \\\n",
       "1        0.17         20.04  Docetaxel|Epothilone B  Epothilone B|Docetaxel   \n",
       "2        0.25         23.41     Docetaxel|Gefitinib     Gefitinib|Docetaxel   \n",
       "12       0.10         17.54  Doxorubicin|Vorinostat  Vorinostat|Doxorubicin   \n",
       "16       0.03         19.33     Epothilone B|PI-103     PI-103|Epothilone B   \n",
       "29       0.13         27.30  Docetaxel|Epothilone B  Epothilone B|Docetaxel   \n",
       "\n",
       "                Combi Name  \n",
       "1   Docetaxel|Epothilone B  \n",
       "2      Docetaxel|Gefitinib  \n",
       "12  Doxorubicin|Vorinostat  \n",
       "16     Epothilone B|PI-103  \n",
       "29  Docetaxel|Epothilone B  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_combi_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare for all lines and drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.341387Z",
     "start_time": "2020-11-17T13:31:50.325659Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_single_kill_df = pred_single_df[['patient', 'drug_name', 'kill']].pivot(index='drug_name', columns='patient', values='kill')\n",
    "#pred_single_delta_df = pred_single_df[['patient', 'drug_name', 'delta']].pivot(index='drug_name', columns='patient', values='delta')\n",
    "pred_combi_kill_df = pred_combi_df[['patient', 'Combi Name', 'kill_C']].pivot(index='Combi Name', columns='patient', values='kill_C')\n",
    "\n",
    "obs_single_kill_df = obs_kill_df.loc[single_drug_list, patient_list]\n",
    "obs_combi_kill_df = obs_kill_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.350423Z",
     "start_time": "2020-11-17T13:31:50.343041Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set(font_scale=1.25)\n",
    "sns.set_style('ticks')\n",
    "\n",
    "cmap = plt.cm.get_cmap('tab10', 10)\n",
    "colors = cmap(np.linspace(0, 1, 10))\n",
    "patient_color_dict = dict(zip(patient_list, colors[0:len(patient_list)]))\n",
    "drug_color_dict = dict(zip(single_drug_list, colors[0:len(single_drug_list)]))\n",
    "combi_color_dict = dict(zip(combi_drug_list, colors[0:len(combi_drug_list)]))\n",
    "\n",
    "drug_marker_dict = dict(zip(single_drug_list, ['o', 'v', '^', '<', '>', 'd', 's']))\n",
    "combi_marker_dict = dict(zip(combi_drug_list, ['p', '*', 'P', 'X', 'h']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### For single drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.389761Z",
     "start_time": "2020-11-17T13:31:50.354880Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(125, 6)\n"
     ]
    },
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % cell death</th>\n",
       "      <th>Predicted % cell death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN120</td>\n",
       "      <td>76.42</td>\n",
       "      <td>96.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN137</td>\n",
       "      <td>79.03</td>\n",
       "      <td>95.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN148</td>\n",
       "      <td>84.00</td>\n",
       "      <td>96.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN159</td>\n",
       "      <td>86.40</td>\n",
       "      <td>90.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN160</td>\n",
       "      <td>56.89</td>\n",
       "      <td>66.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Drug                              File name Replicate Patient  \\\n",
       "120  PI-103  validation_replicates_2019_06_24.xlsx         2   HN120   \n",
       "121  PI-103  validation_replicates_2019_06_24.xlsx         2   HN137   \n",
       "122  PI-103  validation_replicates_2019_06_24.xlsx         2   HN148   \n",
       "123  PI-103  validation_replicates_2019_06_24.xlsx         2   HN159   \n",
       "124  PI-103  validation_replicates_2019_06_24.xlsx         2   HN160   \n",
       "\n",
       "     Observed % cell death  Predicted % cell death  \n",
       "120                  76.42                   96.18  \n",
       "121                  79.03                   95.62  \n",
       "122                  84.00                   96.08  \n",
       "123                  86.40                   90.97  \n",
       "124                  56.89                   66.17  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_df = obs_single_kill_df.loc[single_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug', 'File name', 'Replicate', 'Patient', 'Observed % cell death']\n",
    "\n",
    "pred_df = pred_single_kill_df.loc[single_drug_list, patient_list].stack().reset_index()\n",
    "pred_df = pred_single_kill_df.loc[[d[0] for d in obs_single_kill_df.index], patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug', 'Patient', 'Predicted % cell death']\n",
    "\n",
    "scatter_single_df = pd.concat([obs_df, pred_df[['Predicted % cell death']]], axis=1)\n",
    "scatter_single_df.loc[:, 'Replicate'] = scatter_single_df['Replicate'].astype(str)\n",
    "print (scatter_single_df.shape)\n",
    "scatter_single_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.395256Z",
     "start_time": "2020-11-17T13:31:50.392549Z"
    }
   },
   "outputs": [],
   "source": [
    "# scatter_single_df = scatter_single_df[scatter_single_df['Patient'].isin(['HN137'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.835460Z",
     "start_time": "2020-11-17T13:31:50.397695Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single drug | Pearson r = 0.41 (8.33e-03)\n",
      "Single drug [R-sq -46.40%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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XOz506FAAsrKy2LBhg88xEhMTOXz4MABDhgzBYDBccf0iIiIiUrK33nqLxYsXAxAZGcmzzz7LddddV8NViYhIXaQwpZCgoCDuvfdeAA4dOsS7775b7Jz169ezefNmAEaNGkWTJk2KnTN27FhvKPOPf/yDixcvFjlus9n429/+BoDJZGLChAl+fR0iIiIiUly7du0AaNSoEXPnzqVTp041XJGIiNRVAbWbz+7duzl58qT364yMDO/97777jlWrVnm/DgsLY8SIEcXGeOCBB/jss884efIkzz33HCdOnGD48OGYzWa+/vpr3njjDQCio6N55JFHfNYRExPD//zP//DMM8+QkpLC3XffzZQpU4iLi+P06dP897//JSkpCYCJEycWWatFRERERKrGoEGDcLlcdO/encaNG9d0OSIiUocZ3CXNZamDHnvsMe8UnLK0bNmSjRs3+jx25MgRJk+eTEpKis/jsbGxzJ8/v9h6KZd76aWXeP3110ucLjRmzBjmzJmD0XjlDUJ5eXn07NkTgISEBG2lLCIi9ZLb7eaS1YE/39wEm02YzdXXzOtwOfDnCzAajH55r1EXZWVlkZubS/PmzWu6FPGjHTt2eDu7p06dyrRp08q8Zvz48ezcuROA5OTkIsfmzZvH/PnzvV/PmTOHO+64o9Tx4uPjcTqd3H777cydO7fYcZvNxsGDB0lKSiIxMZHExEROnDiB2+0u9XNIYXa7nW3btrF161b27dvHiRMnyMnJISwsjLZt23LjjTcybty4YruLluT8+fO8++67bNiwgdTUVEwmE1dddRXDhw/n3nvvJTw8vFzjiEiBgOpM8Ze4uDjWrFnD4sWLWbt2LSdPnsTlctGiRQuGDBnCxIkTiY2NLXOcGTNmMGDAAJYsWUJCQgLp6elER0fTpUsX7rrrLoYMGVINr0ZERKT+MBgMGAwG/rbwG348n3tFY5lNRmZP7k/z2Or9gGF12NhxKoE826UrHuuaJlfTOqoFwcYgP1RWt6SlpTFr1ixsNhvPP/98ud67iQAsWLCA0aNHYzZX/qPSrFmzinTFV9SFCxf45S9/SWZmZrFjWVlZ3oBm0aJFPP3002XuRrVnzx6mTZtGenp6kccPHDjAgQMH+OCDD3jttdfUMS9SAQEVpsydO9dnMlwZERERTJkyhSlTplzROH369KFPnz5+qUlERETKZjYZGH59W978aL/P40aDgV7XNCE4yFTqOJ2uiiG2QUhVlFgqs9FMljWbzw99dUXjhFlCGdC2L8Hm+hekpKSkMGvWLM6fPw/A559/7l0Xrz5wu92kZV7i5JlsrDYnwUEmrmoWSePoUG16UA4pKSmsXr2asWPHVnqMwp3p4eHhdOnShWPHjpGWllau6202mzdI6dKlC0OGDKF79+7ExsaSkZHBhg0beP/998nLy+Mvf/kLkZGRDBw40OdYqampPPTQQ2RmZmKxWHjggQcYMGAADoeDtWvXsnTpUk6ePMmUKVNYuXIlkZGRlX7dIvVJQIUpIiIiIhaziT7xzfhs2zGf3SkhQSYmDO+A0ZaNPS/P5xgGA4QGu3BfSMVaxvMZTGbMkTEYg0L9UD0Em4MY1O5GNh/bQZ698t0pN7ftR4i5+sOgmvb999/zzDPPkJ2dDcCvfvUr7rnnnhquqnqcSc/li+3H2bArhcyc4t+50RHBDOnTmhE3tKVZNXdc1RUxMTFkZGTwyiuvMGbMGCwWS6XGufnmm+nbty/dunUjLi4Oo9HI+PHjyx2mGAwGbrrpJqZPn0737t2LHb/xxhsZMGAADz30EC6Xi2effZZbbrnFZ1j20ksveYOZf/3rX96dRwH69etHmzZtvGtFvvnmmyWuCykiRSlMERERkYBjNhkY2b+dz+6UPKuDb747R5/YHFKW/d3n9UEWI6HBFsrzR/zInr8g+oYxV1pyESHmYG5p16/S3SlhllAGt+9f77pS9uzZw9y5c7FaC4KE8ePHc+eddwZ8N4bD6eKDjT/w/rpknK6SF9zJzLGyctNh1mw+wrhhnfj14A6YTfVzPZ2S3Hfffbz44oukpqayatUq7r777kqNM3LkyCuqo2nTprz55pulnjNo0CCGDRvGF198QUpKCgcPHqRLly5Fzjl79iyfffYZAAMHDiwSpHhMmDCBFStW8MMPP7B48WIeeughgoLq188OkcrQT08REREJOJ7ulOaNfP/1fd03KViatCO8TWefx4ODzOUKUgxBIUT1/AVGS/CVlFv8+X/qTgmzVK7bpT52pWzevJlnn30Wq9WK0Whk6tSpjB07NuCDlLx8O0++to0lX3xfapBSmNPlZvEX3/Pka9vIy7dXcYV1y6hRo4iLiwPg1VdfxWaz1XBFpevbt6/3fuFdTT02btyIy+UCCja/8MVgMHDbbbcBkJ2dzY4dO6qgUpHAozBFREREApKnO8WX8xcvse27NBpeP6rYsSCLEWM5P4BHdBmAMaRqpkt4ulMqqj52paxbt44XX3wRp9OJxWLh0UcfZdiwYTVdVpVzOF08u3AHSUfSyz7Zh6Qj6Ty7cAcOp8vPldVdniAO4PTp06xYsaKGKyqd3f5zGGYyFV8Hau/evd77pa3jWPhY4WtEpGQKU0RERCQgVbY7paa7Urx1VLI7pT52pXTu3JnIyEjCwsKYPXs2N9xwQ02XVC1Wbvyh0kGKR9KRdFZu+sFPFQWGESNG0KFDBwBef/31Wt2dsmvXLu99T0dNYUeOHAEgKiqKRo0alThO4V18PNeISOm0ZoqIiIgErNLWTvF0p/S5fhS5J74Dak9XikdF106pj10pAK1bt2bWrFmYTCafHygD0Zn0XN5bl+yXsd5bm8wtPVvVyUVp09PTOXToUJnn5ZWw2LQvnu6U6dOnc+bMGZYtW8b48eOvpMwqcejQIb766isAOnbs6PN7/+zZswA0a9as1LGioqIICwsjLy/Pe42IlE5hioiIiASssnb2WfdNCv0n9SK8TWdyT3xXa7pSPCq6s0996Uqx2WwcOnSIrl27eh/r2LFjDVZU/b7Yfrzca6SUxely88X24/xuVJcyz61t3nvvPd577z2/jzt8+HA6depEcnIyr7/+OnfddRfBwVX7770i7HY7TzzxBA6HA4Dp06f7PC83t+DnXmho2R1uoaGh5OXlVSh4EqnPNM1HREREApqnO8XlcmO3O4rcfjyfzf/tP0dMv1E/daWUb8zq6ErxKLx2isvtwu6wY7dfdnPYCTEH14uulNzcXJ566imefPJJEhISarqcGuF2u9mwK8WvY27YlYLb7Z9wJhAYDAamTZsGwLlz56oksLkSc+fOZd++fUDB9t++dukBvDtblWeLZ88OPvn5+X6qUiSwKUwRERGRgObpTmkaG4rL5Sp2+/T/jhLUtB0N2ncp14fJ6upK8Si8dorT+VPd7stuLhc3twn8rpSMjAwef/xx9u/fj8Ph4OOPP67pkmpEWuYlMnOsfh0zM8fK+cy69yF66tSpJCcnl3krvOtNeQ0dOpT4+HgA3njjjVoTMrz99tssXrwYKFgv6KmnnirxXE83TeGFakviWRsmJCSwf46I+IvCFBEREQl4ZpOBXw24GqPR6L0ZDAbcbjfnMvI4djaP6L634nA4ind9XHYLvaY/Nkzs27ePb7/9lqSkJLKysqq0fk93isn0U/2GorfI4HCGxN0Y0F0pP/74I48++ijHjh0D4Prrr+exxx6r4apqxskz2VUy7okzVft9XNcYDAbvzj5paWksXbq0hiuCjz76iLlz5wLQpk0b3njjDSIiIko8Pzy8oIPu0qWypwl6zgkLC/NDpSKBT2GKiIiIBDyL2UTf+GZc1bwBFosZi8WMwWjAYDBgMZtoHBOKpWlbQlp3xuVyl3jDHExkz19w4tRpMjIyyMzMJD09nVOnTlVp/Z7ulIigcCxmCxZL0dvgq28itIK7/tQlR48e5dFHH+XMmTMA/OIXv+DRRx/1Tkuob6w2Z9WMa6+aceuyIUOGeNfmeeONN2p0PZGvvvqKmTNn4na7adq0KQsXLix1hx6Apk2bAnj/7ZQkKyvL+9o814hI6RSmiIiISL3gWTvFw2Q0YTQaGdCzFeGhZjKzcmnYbxRGo6HEW4NrB+IyBWGz2YiOjiY6OprY2FhatWpV5fUXXjulsEDfwWf//v08/vjjZGZmAjB27FimTp2KyWSq4cpqTnBQ1bz2YEv9/W9aGs/aKenp6SxZsqRGati1axfTp0/H4XAQExPDW2+9Va6fO54dfrKysjh//nyJ53k6vgpfIyKlU5giIiIi9YJn7ZTmjQra3o1GA6EhFkbf0oGoiDAaN2lCaMsORLTrWqzzw2KxEBQeSUzvEYRFNuDaa6+lR48e9OjRg27duhEVFVXl9RdeO6WwQN7B5+DBg8yaNcv7F/P777+f8ePHYyjn9tWB6qpmkVUybptmVf99XBcNHDiQa6+9FoA333zTu0NOddm/fz9TpkwhPz+fiIgI3nzzzXIHHr169fLe3717d4nn7dq1y+c1IlIyhSkiIiJSb1zenXJDtxbERP68kKzBbKFBn1t9XludO/iU5PLulEDvSomLiyMuLg6TycSMGTO47bbbarqkWqFxdCjREf5dADk6IphG0YEZyvmDpzslIyODd999t9qe98iRIzzwwAPk5OQQGhrKa6+9Rpcu5d/CevDgwRiNBR/5Vq9e7fMct9vNhx9+CEBkZCT9+hXvgBOR4hSmiIiISL1RuDvFbDJw603tCQkye48bjCaCm7UnuFWnItdV9w4+Jbm8OyWQu1KgYCeSv/71rzz11FMMHDiwpsupNQwGA0P6tPbrmEP6tK73HT+lGTBgAD179gTgrbfeqpZtpFNTU5k0aRIZGRlYLBbmzZtH7969KzRG06ZNGTlyJFCw5sqGDRuKnfPOO+9w6NAhAO699956uxaRSEWZyz5FREREJHB4ulMOncwo0pXi4elOOXcq2ftYbehK8fB0p2w+tiPgulJcLhfbtm3jxhtv9H6wj4yM9E6xkJ+NuKEtazYfwem68g/1JqOBETe0vfKiAtzDDz/MpEmTvOv3lCYtLY0tW7YUewwgNzeXVatWFTk2YMAAGjdu7P06IyODSZMmcfbsWQCmTJlC06ZNvaGHL7GxscTGxhZ7fMaMGWzdupXMzEymT5/OAw88wM0334zD4eCLL77w7lJ01VVXcf/995f52kSkgMIUERERqVc83Smd2zYs0pXiUbg7xXoqudZ0pXgU7k4JpK4Uh8PByy+/zObNmxk7dizjx4+v6ZJqtWax4Ywb1onFX3x/xWONG96JZrG1Iyyszfr370/v3r1LXXvE4+jRo8ycOdPnsczMzGLH3nnnnSJhyqFDhzhx4oT363nz5jFv3rxSn3Pq1Kne6UiFtWzZkgULFjB16lQuXLjAK6+8wiuvvFLknNatW/Paa68RGVk16/GIBCJN8xEREZF6x2wyEBVecjhSeO2U2tSV4hFiDubGq/oETFdKfn4+f/vb39i8eTMA27dv59KlSzVcVe3368Ed6BZXvBOhIrrFxfLrQR38VFHg8xVW1AXXXXcdH3/8MZMnT+bqq68mLCyMiIgI4uPj+Z//+R8+/PBD2rdvX9NlitQpBnd1TPiTKpeXl+edx5mQkEBYWFgNVyQiIlK3uWz5nP/ivzQc9FvMkQ1rupxiHC4nZmPd38o2KyuLZ555huTkgmlVHTt25Mknn6yWHZICQV6+nWcX7iDpSHqFr+0WF8sT9/UjLMRSBZWJiAQ2hSkBQmGKiIiIf7ldTpx5WRiDw2rNFJ9Ak5aWxlNPPUVKSgoAPXv25LHHHiM0NLSMK6Uwh9PFyo0/8N665HKtoWIyGhg3vBO/HtQBs0mN6iIilaE1U0RERER8MBhNmEIjMZj0dqkqpKSkMGvWLM6fPw/AzTffzPTp07FY1CVRUWaTkbt/0YlberXii+3H2bArhcwca7HzoiOCGdKnNSNuaKs1UkRErpA6UwKEOlNERESkrsjIyGDq1KlkZ2cDMGrUKB544AGMRnVJ+IPb7eZ8Zj4nzmRhtTsJtpho0yyKRtEh2v5YRMRP9KcWEREREalWMTExDBs2jJUrV3LvvfcyduxYfcj3I4PBQOOYUBrHaLqUiDJ+Kv0AACAASURBVEhVUZgiIiIiUge5HA7cdrtfxzQEBWE0Vc+ithMmTKBXr15069atWp5PRETEnxSmiIiIiNRBBqMRe04OJ5d/AFc4a9toNnPVuLuxVOHCr5s2baJPnz5EREQABd0TClJERKSuUpgiIiIiUgcZjEbMkZG4bDYu7ku6orEaDbgRY3DV7FjkdrtZsmQJy5cvJz4+ntmzZxNcRc8lIiJSXbTKl4iIiEgdZQwKosWoUXAF640YzGZa3DoSU0iIHysr4HQ6WbBgAcuXLwcgNTWVtLQ0vz+PiIhIdVOYIiIiIlJHGYxGwlq3pEH3rpUeI/aGfliiG/ixqgI2m40XXniBtWvXAtC0aVOef/55WrVq5ffnEhERqW4KU0RERETqsCvpTqmqrpS8vDxmz57N9u3bAWjbti3PP/88LVq08OvziIiI1BSFKSIiIiJ12JV0p1RFV0pGRgaPP/44SUkF67jEx8fz3HPP0bBhQ78+j4iISE1SmCIiIiJSx1WmO6UqulJcLhdPPfUUR48eBaBv377Mnj3bu4OPiIhIoFCYIiIiIlLHVaY7pSq6UoxGIxMnTsRkMjF06FBmzpypnXtERCQgaWtkERERkQDg6U65mLgf3O5Sz63KHXx69erFP/7xD9q3b4/hCnYZEhERqc3UmSIiIiISACrSneLPrpQdO3Zw6NChIo/FxcUpSBERkYCmMEVEREQkQJRn7RR/dqV8+eWXzJkzh9mzZ3Pq1KkrHk9ERKSuUJgiIiIiEiDK053ij64Ut9vNypUrmTdvHi6XC6vVSlpa2hWNKSIiUpcoTBEREREJICV1p7hx+6UrxeVysXDhQhYtWgRAREQEzz77LD179ryiukVEROoShSkiIiIiAcR3d4obu9N+xV0pDoeDl19+mQ8//BCA2NhY5s6dyzXXXHOFVZfO6XRhszur9DlEREQqQrv5iIiIiASYy3f2sTsd5OOk2cgRle5Kyc/P54UXXmD37t0AtGzZkqeffprGjRv7s3Sf7E4XVpuTIIupyp9Lar8dO3YwYcIEAKZOncq0adPKvGb8+PHs3LkTgOTk5CLH5s2bx/z5871fz5kzhzvuuKPU8eLj43E6ndx+++3MnTu32HGbzcbBgwdJSkoiMTGRxMRETpw4gdvtpmXLlmzcuLHMmlNTU9m6dSuJiYl8//33pKenc+HCBQwGA7GxsXTt2pVbb72VYcOGlbjg86pVq5g5c2aZz1VYef+bitR3ClNEREREAkzh7pSL+xKxOq00v/kW7OHBWB1Wgs3BFR7ztdde8wYpHTp0YNasWURFRfm79GKcThfJxzM4cCydOwZdTUiQ3r5K1VqwYAGjR4/GbK7899qsWbNYtWrVFdWxfPlyXn31VZ/HUlNTSU1NZe3atVx33XXMnz+fhg0bXtHzebRr184v44gEOv02EhEREQlABd0pt3I+IQGX0UiLW0ey9uQ2BrXrT+NKhCn33nsvSUlJtGjRgpkzZxIaGloFVRdnd7r4eOsRUs5mM+L6tgpTpMqlpKSwevVqxo4dW+kx3G639354eDhdunTh2LFjFVqo2Wg00rlzZ3r37k2nTp1o0qQJsbGxZGdnc/ToUZYtW0ZycjJ79uxhypQpvP/++xiNRVdxGDp0KF27lr5dutvtZsKECWRmZhIeHs7QoUMr9mJF6in9NhIREREJQAajkdDWrYjo2pnoqCjs4UFs3bcTu9POnV1GVrg7JTY2ljlz5hAdHY3FYqmiqovydKX8kJIJwBffHOOOQR0UqJTB7XbjzDqPLe0kLrsNoyWIoMZXYYpqVOJ0ECkQExNDRkYGr7zyCmPGjKn09/rNN99M37596datG3FxcRiNRsaPH1+hMGXq1KlMnz7d57EbbriBcePG8cgjj7B27Vr27dvHxo0biwUhUVFRZXaQ7dy5k8zMgn9jw4cPJ8QP26aL1Af6TSQiIiJF5NscGA2GMtensNocuEEfbGspu9PB2fwLtBg9CnN4BF+c2onVaWN7yl6GXX1Lmd0phw4d4ujRo4wYMcL7WHWsj1KYpyvFY/PeU4y4vp2+50pgzzxL1t515CRuwpl7sdhxU3gDIroPIqrXMCzRTWugwtrvvvvu48UXXyQ1NZVVq1Zx9913V2qckSNHXnEtJlPpP4ONRiP3338/a9euBWDPnj2V6irxLCgNcNttt1X4epH6Srv5iIiIiFe+1cEnW49x7PTFUndPsdoc7PruLDv2nyHf5qjGCqW8HC4H7+//iKAWzbGHB7H95B4A7E476w5vxuqwlnhtQkICTzzxBAsWLOCrr76qpoqLurwrBSDf5uSLb47pe+4ybqeDjC0rSHllGhe3r/EZpAA4cy9ycfsaUl6ZRsbWD3A79d/xcqNGjSIuLg6AV199FZvNVsMVlS4sLMx7vzK1Wq1WbxjTvHlz+vXr57faRAKdwhQREREBCoKU1ZsP8/GWI/zrvb0lBiqeIGXhR/t5+5MDClRqIbvTwbc/HuCH9OOsObyBD374Eqvz5w9a21P2kmXN9Xnt119/zTPPPEN+fj4Gg4H8/PzqKruIy7tSPDbvPUXeJX2/ebisl/hx6dNkfP0+uMq5fbTLScbm9/hx6dO4rJeqtsA6xmg0MnXqVABOnz7NihUrarii0n3++efe+5VZOHbDhg1kZ2cDMHr0aE0DE6kAhSkiIiJ1lMthx+Uo+y+RbpezzPM8QcoX248DkGd1+AxUCgcpTpcbl9utQKUWcrgcrD28GZfbxeZj37A7ZR92u917y7PmsfaHr4p1p3zyySe8+OKLOBwOzGYzf/nLX4pM86kuvrpSPNSd8jO308GZ5XPIP3mgUtfnnzzAmeVz1KFymREjRtChQwcAXn/99VrXnZKRkcG+ffu83WMA0dHRjB49usJjffTRR977muIjUjGacCoiIlIHuRx27OdP4XbaCWraFqM5yOd5bpcTZ+5FrKd/ILR9D4yW4utkXB6keHgClUfG9aJdiwa43e4iQYq3lp8CFYB+XZtpPYsa5ulK+TH7HE6nC5fLVfwkN2w9vosRHQYSbA7G7XazdOlSli1bBkBoaCiPP/441157bTVXX6CkrhQPrZ1SIHP7mkoHKR75Jw+QuX0NMTfd6aeqqld6ejqHDh0q87y8vLxyj+npTpk+fTpnzpxh2bJljB8//krKvGIPP/ywdzrO5aKjo5k/f36Ftyq/cOECW7ZsAaBr167e6U0iUj71+zeQiIhIHeQJUs599G/cTgdNRk/zGah4gpRzH/0be/ppGg6+l/BO/YoEKnaHixNnsvlyxwmfz+UJVKbd3ZO0jEss+vRAkSDFW5PbzbL1yXRpH0uQxYRRreI1xtOVAgULWBoouk0rAAZw4mTt4c38ustIFr35Np9++ikADRo0YNasWVx99dXVXHkBX10pLpcbp7OgQ8pkMnm7U+rzzj72zLNkbFnul7EytiwnouuAOrko7Xvvvcd7773n93GHDx9Op06dSE5O5vXXX+euu+4iOLjiW4pXtXHjxjFt2jRiY2MrfO1nn32Gw1HQlaSuFJGK0zQfERGpNxzlXE/AWd51B2pA4SDFdSkbt+0S5z6ah+3s8SJTeYoEKedPgdvFhY2LyU3egcv+89QOi9lIm+aRTBrVFZPRdwCSZ3Xw/Du7WPjxfp9BCkBYsJmH7+pJVHiQgpQaVLgrBcBoALPZhMViKXozWzAajGxP2UtGbqb3A1WTJk14/vnnayxIKXgNxbtSnC4nLldBl43n32d9Xzsla++68q+RUhaXs2A88TIYDEybNg2Ac+fOVUlgUxEzZ87k448/5uOPP2bJkiU8/vjjtG/fnmXLlvHYY49x7ty5Co/p2cXHbDYzatQof5csEvAUpoiISL2Q77By6uKPWMtYO8TqsHHy4ulSdzqpKZcHKR6XByrFghTvib4DlZAgM326NC01UClNWLCZ6b/pRfuWDcrcTlmqVuGuFPDRkXIZu9POxuPbuOOuX3P11Vfz/PPP06JFi6ous0QlrZViMpowGo0YjUZMxoLvsfq8dorb7SYncZNfx8xJ3FTm90ttNHXqVJKTk8u89e3bt8JjDx06lPj4eADeeOONGluMGQp22unYsSMdO3akd+/eTJw4kQ8//JBBgwbx9ddfc+edd3L69Olyj3fs2DESExMBGDBgAA0bNqyq0kUClsIUEREJePkOKx99/yX/2PoqB9MOlRioWB029p5O4oUtr7D+yNZaFai43W5c+TmkfTy/SJDiPV4oUHHmZhYPUrwnFgQq1h+P4HbYvQ9XNlBRkFJ7XN6VAm7ctnxcDjtQ8ofk7Sl7MYdZeO655yo1VcCfSlorxWg0YLGYsVjMGAt9f9bX7hRn1vkStz+u9Ji5F3Fmp/t1zLrOYDB4d/ZJS0tj6dKlNVxRUUFBQfztb38jLCyMs2fP8ve//73c13q6UkBTfEQqS2GKiIgENE+QsuHIVuwuB2/uft9noOIJUt75diUut4uPvv+yVgUqBoMBY3AY0TfeAUbfoYXbdolza/7Fj+//zXeQ8pOI+BsJbtYOg9lS5PGKBioKUmqX4l0pLtwOG2570e9hp8tJbl4u7p8CFrvTzrrDX2Mw1+z0rNJ28ClJfe1OsaWdrJpxz/leO6k+GzJkCF27dgUKulMqspBtdYiJiaFXr14AbNy4EbvdXsYVBeG8ZxefyMhIBg8eXKU1igQqhSkiIhKwCgcpHoUDFdtPgcrlQYpHbQtUjJZgwjr0JnbIhJIDFYcNV15WiWNEdBlAzM13YQwKJSsri6SkJL799lvv7fSpk/S6phGd28ZgtztKvQ3o0YI2zSI4dvQwWVklP6dUPd9dKT9937qc3u4Uh9NBVlYW1nwrubk/fyjcnrKXbGtutdddWFk7+JSkPnanuOxVs1VvebZar488a6ekp6ezZMmSGq6muOjoaADy8/PJyMgo8/w9e/aQmpoKFGwDXRsX1hWpCxSmiIhIQLI6rHx6aEORIMXDE6gc+KlDxVeQ4vHR91+y6ej2MtdaqS7lCVRKUjhIATh16hTp6elkZmaSmZmJ1WqlYWxjdh88y7fJZ70LfpZ0W7/zOEdTL9IwtnGF5uqL/5XUleL92m7F7S7YHtb90yLChafL2J121h7eXGPBYWW6UjzqY3eK0eJ7K/QrHreELdbru4EDB3q3CX/zzTfJza3Z4PFyhRefDQsLK/N8T1cKwJgxY6qkJpH6QGGKiIgEJAMGOjRsT5DJ4vO43eXgjd3v8/be5SUGKQCh5hDiGrbBUIt2qKlMoHJ5kALQqlUrYmNjiY6OpmnTpnSO78a+I5m88WESbgzeBT9LuuXbXPzzvT2kZblo07Z9Vb1cKUOpXSmeR5wOXA4bIT/9BTosLIyw0KIfumqyO6WyXSke9a07JajxVVUzbpM2VTJuIPB0p2RkZPDuu+/WcDU/O3v2LAkJCQC0aNGCiIiIUs+32Wx8/vnnQMHvgOuuu67KaxQJVOaaLkBERKQqBJmDuKZRe37f+7f8d/cSbM7i88gdLgd7f9xf4hih5hD+2G8ibWJalRjK1BSjJZiwjn2wpadyYcenpZ4b0uJqogeMLRKkAERFRdGtWzcArDYHu747y6JPD2I0mcrd9GJ3wrzl3/LIuF60a6G1U2qCw+Ug32FlcPsbgZ+6Ugqvk+J243a7MRiNGCzBuFwuTCbfbwFPZ58lMjicYHP1tf1fSVeKh6c75Y5BHQgJCvy3t6aoRpjCG/h1EVpTeANMkTW7AHFtNmDAAHr27ElCQgJvvfVWle98dOHCBXbv3s2wYcNKPCcnJ4c///nP3nVSRo8eXea4mzZt8k7LHD16dK36Q4FIXRP4v21ERKTeKk+gUpLaHKQAuF1OXJdyyD2yD5er9Df1+edSsJ49SUiL9j7b+D1BysKP9uMsYyxf8qwO/vXeXgUqNcRkNNH/qoK/LrtdLuyZZ3HlFgQTTpcLl9P505kGQpu0xhTeoNQPUEZD9f7/u9KuFI/Ne08x4vp29SJMMRgMRHQfxMXta/w2ZkT3QfpgXYaHH36YSZMmkZlZdvCXlpbGli1bij0GkJuby6pVq4ocGzBgAI0bN/Z+nZeXx7Rp02jbti3Dhw+ne/fuNGnSBLPZTHp6OgkJCaxYscI7xScuLo4HH3ywzLq0i4+I/wT+bxsREanXKhOo1IUgxZl7kXMf/Rtn5pkia1/45LBy/tP5NBn9MEFN2xYJVOwOF8d/zC41SAkLNjP1rp6kZV7inU8P+Dwvz+rg38sSeOrB/sQ2CMGoD2XVpvD3qD3jDGeXzwWnA4fD4f0LtMFgIDIyEluT1jS7+3GMlpCaKrcIt9uN0+lm0q+6lrZ7c7kZjGB3OLGYAz/Qi+o1jIs7PgaXs+yTy2I0EdWr5A4IKdC/f3969+7N7t27yzz36NGjzJw50+exzMzMYsfeeeedImGKx/Hjx3nttddKfa6bbrqJuXPnEh4eXup5GRkZfP311wD06NGDtm3blnq+iJROYYqIiASUrKwsTpw4gdP58wcMs9lMm7atmdhjLAu+WVTq9QYMPHj9PTQKiiH54PcAtGnThqioqCqtu7wKByn286cwGAxYLGUHPm5bPuc+mkeT0dOKBCoWs5E2zSIZ0qcN63YcL3ZdWLCZ6eN60b5FA65u1QCTwcBbnxQPXowGA3cO6UhkmEVBSg1x2fK5uPtzcBasHWI2mwkLCyM/P5/IyEhMJhP2Cz+SdySBsA59MJprPig0GAyEh1oID635WuoaS3RTYgbcRcbm9654rJgBd2GJbuqHqgLftGnTmDhxYpU/T4sWLViyZAlbt24lMTGR06dPk56eTl5eHuHh4bRs2ZJu3boxatQo+vbtW64xP/vsswpNCRKR0hncVT3hT6pFXl4ePXv2BCAhIaFcK3mLiASigwcPFtnZACA0NJRO8deQcO4Ai/auKHOMh/pN5OoGbUg++D1Wq5UmTZoQHx9fVSWX2+VBSmUYgkKLBSoA+VYHKzcdLhKoFA5SPFN3rDYHOw+cLRKoGA0GJtwazw3dmteLKRa1lT3jDKeXPOUNUzzcbneR6RuWhs0LulMuW0NH6h6308GPS58m/+SBSo8RclUXmt/zJIYS1tERERHftJuPiIgElMI71Hh3qekaz/70ZJZ+uxqjwVjm7c3d73E0O4X4bl1o1qwZrVq1qumXhdvtxnUpp8wgJbxzf6JvurPEXX7ctkuc+2ge9rRTuBw/T3kKCTbz60FXM6xfW8B3kAIQHGSmb5emTBrVFZPRoCCllnDZ8rm46zNyLmbichXdmerydTA83SmF//9L3WQwmWl210xCrupSqetDrupCs7tmKkgREakE/eQUEZGAUniHGgCrw8be00ksSVyD0WzCSPnWUng7YTn39/4N8Vd3JNjHoq3VzWAwYLAEE9K6c4lhimf7YwxGTKGRpG94x+d6CkGNWmFu0BjDZYGLJ1CxmI1079CoWJDi4QlUABwul4KUWsCRk8GPu77EYbPhdDqJiooqdTHRrN2fExbXE9D0mrrOGBxK83ueJHP7GjK2LC/fGipGEzED7iL6hjEKUkREKknTfAKEpvmIiBTnCVLe+XYlLrer7AsuYzGaCwKVxrUjUIGCDoTMbz4kO+HLIo97ghTP1A2X3UreD7uLBSrBLTrQ+NaHMIaEYzD6blDNtzkwGgxl7spjtTlwg4KUGma/lMuP694mfe8GAIKCgggPj6CspWtih91Xa9ZOEf+wZ54la+86chI3+dw22RTegIjug4jqNUxrpIiIXCGFKQFCYYqISFHlCVJCzSH0a92TbSd3l7jLjydQ6dK4I0G1NFC5PEjxnndZoFKeIEXqlrS0NIJs2Rx78zHcTgfBwcFl7ujhobVTApfb7caZnY7t3AlcDhtGcxBBTdpgiozV9sciIn6iPyWJiEiAcpNlzaGkvxl4tj9uG9OKLk06lbhtstPtIteW549dW/3GGBRC9PW3AQW79PgKUgCMlmDCOvQGIPe77TT65WQFKQFk3759NG8cS+bOz3A7HYSGhhIaWv5gpLbt7CP+YzAYMEc1whzVqKZLEREJWHo3JSIiASnYHMzAdtdze/wIDBT9S6wnSGkT0wqLycI1jdrz+96/JchU9AOl0WDknu5j6NOyR62Z5uPhCVRibr671M4CT6DSeJQ6UgLJ1q1b2b17N0EuKxlJXxMWFl6hIMUja/fn4HKUfaKIiIgUoXdUIlIt7HYnuTlWHPbSF8ZzOl3k5diw2fTmXq6cr0ClcJDiCU+CzEHFAhVPkNK3Ve0LUjyMQSEYg0LKPs8SjDE4TEFKgMjLy2PRokWMGjGMi7s+Jzw0hJCQ4EqNpZ19REREKkfvqkSkytntTlJPZrL0jZ2cO5NdYqDidLq4mHGJ99/axQ8HzylQEb8oHKiEWUKLBSkehQOVEHNwrQ9SpP4KCwvjueeeI9TgwHFyH0HBIWAwVvqWtXedulNEREQqSAvQBggtQCu1lSdI+Xj5Puw2JyGhFsaM60GTZpGYC+0U4glSVi3eS9bFfIxGA7/4VTwd4psQpJ1CxA+sDisXrTlEh0QVC1IKszlsZOZn0yAkUkGK1FpupwOX3erHxUQNGIJCtDipiIhIOekTiohUmcuDFID8S3bWvPdtkUDl8iAFwOVy8+XHBwEUqIhfBJuDaWSyYDSU3pQZZA6iUXhMmeeJVBe73c6///1vBg4cyHXXXQeAwWTGZNLPRRERkZqid4oiUiV8BSkenkDl3Jlsn0GKhydQ+eHguWJjiFRGeQMSBSlSW+Tl5fH000+zefNm5s6dyw8//FDTJYmIiAjqTBGRquJ2k30xH4fd5fOwJ1Dpc2Mb9u06VSxI8XC53FzMuIS7Vm1MKyJS9S5evMjs2bM5fPgwAO3bt6d58+Y1XJWIiIiAwhQRqSKWIDPXdG2G2+1mw6ff42t5pvxLdrasP1zqOP1ubk/vG9vU2DQft8uF2+nEaCl5jQ0Pl91ervNERMpy9uxZZs2axenTpwHo06cPf/nLXwgOrtyuPSIiIuJf6mMWkSpjCTLRuVtzhtx6TaUWNex3c3v61HCQ4sjN4+KBAzit1lLPdebnk5nwLc583x02IiLldfz4cR599FFvkDJ48GBmzpypIEVERKQWUZgiIlWqsoFKbQlSjix4lcPzXiFj954SAxVnfj4pK1ZyeMFrpK7+SIGKiFTawYMHmTlzJhcuXADg9ttvZ/r06ZjNaiYWERGpTfSbWUSqnCdQwQ3rP/0OALfLjdPpLLIWigEDJpOJvgPa1ZogJeu77wE4tnARADG9r8NU6K/DniDl3IZNAJxZuw6AlrePxhQSUs2Vi9RNbqcdt9O/i0wbjCYM5ro37W7ZsmXk5uYC8Lvf/Y477rijhisSERERXxSmiEi1MJoMtLgqGqPRgMvlxuly4nIVXZzWjRsMcFW7hhiNFZ8W5A++ghQAt9NZLFC5PEjxUKAiUjFup5Ozq17EmZvpl/FM4dE0veNPGKh7Ycr/+3//j7/+9a+MGjWKIUOG1HQ5IiIiUgKFKSJS5TzbH69ZmoDLVdCJYjKawE3xzhSjiU9XJjFmXA+aNIvEbDFVa60uq5Xjby0qEqR4FA5Uonv24NTK1cWCFI8za9dhjoyg6S+GYgoOqtKaRQKBMzcTZ05GTZdRI9xut3caZEREBP/4xz8wmar3Z5+IiIhUjMIUEalSniBl1eK9RbY/NhgNmI2+fwR5tk2ukUDFZKLZyBFcOnsGR2YWjp/a7T08gUpIi3VcSjlV4jAhzZsT268vBrM+EImIby6Xi0WLFmE0Gpk4caL3cQUpIiIitZ/CFBGpMiUFKeVRmUDF4XDhcrrKPK90RoJbt6HTE3/FnX+Jg0886TNQKStI6TRjOpaGMRj1oUhEfHA4HMyfP5+NGzcC0LhxY0aOHFnDVYmIiEh5KUwpgc1mY9WqVaxdu5bk5GSysrKwWCy0aNGC6667jnvuuYdrrrmm1DHsdjvLli3jk08+4dixY+Tn59OsWTMGDBjAxIkTad26dTW9GpHqV94gpWmLKM79mI3b7S52rKKBisvp4oN39pCTU/o2xuURERHMr+/tiTk6qliYUhoFKSJSFqvVygsvvMCuXbsAvO8tRKR8duzYwYQJEwCYOnUq06ZNK/Oa8ePHs3PnTgCSk5OLHJs3bx7z58/3fj1nzpwyF3+Oj4/H6XRy++23M3fu3GLHbTYbBw8eJCkpicTERBITEzlx4gRut5uWLVt6g9TKuHTpEqNGjeLUqYI/7JR3vK+++orVq1eTlJTE+fPnAWjUqBFdu3bl9ttvZ9CgQZWuSaQ+Upjiw6lTp/j973/P0aNHizxut9s5fPgwhw8f5oMPPuDhhx9mypQpPsc4f/48v//97zl48GCRx48fP87x48dZuXIlf//73xk6dGiVvQ6RmuRyufku8cdSg5R+N7end/82HDpwhvWffF9ioJK45xSDf1l6eOmRk2MlJ+vKwxQAg9FIu0m/49BL/8KZd6nM8xWkiEhZcnJyeOaZZ/juu4KdzeLi4pg1axbR0dE1XJmIeCxYsIDRo0df0Zbks2bNYtWqVX6s6mfz58/3BinlYbVamTFjBuvXry92LDU1ldTUVNauXcvQoUP55z//SVCQ1noTKQ+FKZex2+1MnjzZG6R06tSJSZMm0a5dO3Jzc9mzZw9vvfUWeXl5/POf/6RVq1aMGjWqyBgOh4M//vGP3iDl1ltv5de//jXh4eHs3r2bV155hZycHGbMmMHSpUvp2rVrtb9OkapmsZjoc2Nb8i/Z2be7+C/8fje3925/3KlrMwCfgUpcp8YMHN6JoGD//7jytT2zh8Nhwul0YWzSmDaTJnLo5XmljmUMCuLq6X8kxwApSUlFjplMJtq0aUNUVJRf6xeRuiU9PZ2nnnqKEydOANC9e3cef/xxwsLCargyESksJSWF1atXM3bs2EqPUfj9THh4OF26dOHYsWOkpaVd7ah0ZgAAIABJREFUUW3ff/89b7/9NsHBwZjNZu9W6qV55plnvEFKbGwsDzzwAPHx8ZjNZg4dOsQbb7xBamoq69ev55lnnuGZZ565ohpF6guFKZdZv349hw8fBqBnz54sWbKkyEJwN954I4MHD+Y3v/kNdrudV199tViYsmrVKr799lsAfve73zFz5kzvsR49etC3b1/uuecerFYrzz33HEuXLq2GVyZS/YKCzQwY2gGgSKBSOEgBCAoyc03X5kDRQCWuU2OG39aFkNCq2d7U1/bMHm6XG3BjMhrJTNrv3YWoJK58K1nJh4i4tjtWq5VLl4p2sphMJuLj4/1VuojUMampqcyaNYtz584B0L9/f/70pz9hsdS97Zul9nO73aTnZXDy4mlsThtBpiCuatCC2LAY785R4ltMTAwZGRm88sorjBkzptL/Rm+++Wb69u1Lt27diIuLw2g0Mn78+CsKU1wuF08++SQOh4OHHnqIlStXlhmmnD9/npUrVwLQoEEDVq1aRbNmzbzHe/fuzejRoxk9ejSpqal88MEHPPLII8TGxla6TpH6QmHKZTwhCMCDDz7oc0X9rl27MnDgQL788kt++OEHcnJyiIiI8B5fuHAhUPDDeMaMGcWu7969O2PHjmXp0qXs2bOHxMREunfvXgWvRqTmXR6oXB6keFiCTEUClfYdG1VpkAK+t2cufMxoNHLm07Wc27gJo7HsN5/HFr5N+/vvo1uP7pxITcVqLZhuZDKZaNWqld/rF6kJWVlZnDhxAqfT6ZfxLBYLHdu3xeFw4LDb/TKm2+HA6XTy3YED2P0wpj+6y/7v//7PG6SMGDGCyZMna9ce8btzOedZd2QLm49t56I1u9jxBsGR3NLuBobFDaBJRKMaqLD2u++++3jxxRdJTU1l1apV3H333ZUapyoWlF66dCn79u3j/7N353FRlvv/x1/3rCyCoCAuKCCu4J561CKtTMus1NJWMztZp9Ky7JfV6ZtamXbqHFtMS21RSzNzadHj3mKZueQKrqioKAoIjizDMDP37w/OTCDrwM3+eX4ffB8Tc9/XXHMKuO/3fK7PFR4ezrhx49whSUn27dvn/uBoxIgRBYIUlwYNGvDwww8zffp0nE4n+/bt48Ybb9R8/kLUNRKmXCX/RVdJDWLzP5f/nPj4eE6ePAnArbfeitlsLvL84cOHuytSNm7cKGGKqNNcgUqr1o0Ji2xUKEhxcQUqvr5mmrcKqNQgBUrenlmnV1DtdtJ+347BUPZ5nF64mAj9GNr1vAZ9MT//QtRmZ8+eJTU1VbPxzGYzqqridKqlVoCVldOpoqoqFovFHWpWVEWry0aOHMnFixdp1KgR9913n1QHCE3ZnQ5WH1rPitg1ONTid7W7nHOF7w5vYM2RTdwdfRt3dhyMQSehXn5Dhw5l9erVxMfH89FHHzF8+PAa0UPkwoULzJo1C8jrx1LWOZX13qZVq1ZFniOEKJ6EKVcJDw93Pz5z5gxt27Yt8rgzZ84AeeVygYGB7u//+eef7se9evUq9nWioqLw8fEhKyurwDlC1FUms4GIto0xGEq+aDOa9LSKbFTqcZVKVXFac1Dtds9PdTg4+elCAAIlUBF1UGhoKA6HQ9PKFEVR0OmUMlWAlYVOp6AoCv7+/ppVppSnuszhcLirTxRF4amnnpIQRWguO9fKW1vnEJd8rMznOFQnyw5+z4ELh5kc8yTeRq9KnGHtotPpGD9+PM8++yznzp1j+fLlPPDAA9U9Ld544w0yMjIYOnQo/fr1K/N5V9/bFOf06dPux2FhYeWaoxD1jYQpVxk6dCjvvvsumZmZzJ8/n/79+xcqw42Li+Onn34CKNSYKv8OQK1bty72dQwGA61ateLw4cPEx8eXOq+srKwSn7+6P4MQNVFZA5LqDlIc2Vaciq7YQ7yaNaNJ/+tI/O6HInf5cQcqikLgNT0kUBF1ir+/P507d9Z0TKfNisFgQNGof4jeYECv1xMdHa3JeOWxdu1afvnlF6ZNm+auUpUgRWjN7nR4HKTkF5d8jLe2zuGVAc9IhUo+t9xyC3PmzOHYsWPMmzePkSNHVmt1yubNm9mwYQN+fn68+OKLHp3boUMHunfvzp49e1i1ahVjx44lJCSkwDEZGRksXJj3QVDXrl3p0KFsOygKUd9JmHKVRo0a8dZbbzFp0iT+/PNP7r77bsaMGUN4eLi7iuTTTz8lNzeX3r1788QTTxQ4Pykpyf346l9UV2vatCmHDx8mLS0Nm81W4i/p7t27V+yNCVEJtO6dUBFGo5HWEW2w2+3l/iRaURT0Oj2KXo+q5nVSsdvt2O1/jefdvBltJo5H7+eHV6tWHHn3fRz/Czv1eoP7Zknn5YU5OFhunoSoZ1RV5auvvmLp0qUAvP/++/y///f/qnlWoq769tD6cgcpLnHJx/ju8AZGRN2q0ayqVmpqKkePHi31uNI+mMzPVZ3yzDPPkJSUxLJlyxg9enRFpllumZmZ7t11Jk6cSHBwsMdjTJ8+nXHjxpGYmMiIESPcu/no9XqOHTvGggULOHv2LCEhIbz55ptavwUh6iwJU4pw8803s2LFChYsWMDq1auZPHlygeeDg4N57rnnGDVqVKEAJH9H7dK2OvT29i5wXk1YjymEJ7TunVARrr4LqlP93048nlPJO9dgNGLwzvuELn8fB+/mzWn33LOk5+Zycs8eotq1o90zEzg8yxWoODAYDOh9fWk3cQK+Ya3Qyc+1EPWGw+Fg/vz5rF27Fsir4hk2bFg1z0rUVRczUvgmdo0mYy0/+APXtepVK5vSLl261B1eamnw4MG0b9+eI0eOMG/ePEaNGlVsL8TK9O6773L+/Hmio6O5//77yzVGZGQk33zzDV9++SULFixg5syZBZ43Go2MGzeOMWPGlCusEaK+kjClCDabjVWrVrmX8lwtOTmZ7777joiICK699toCz+VvdFfaVmr5w5PSGuTt2bOnxOezs7M9Wj8phBa07p1QEa6+C4ou76siHE4HisGMzmhEZ9Cj0yl4N29G+0nPkqUoXExJwdfXl7NJSYSHhtLxuWc48u77kGOTIEWIeio3N5dZs2bx66+/AtCkSROmTZtGixYtqnlmoq7aEL+1xGaznnCoTjbGb+WBrsM1Ga8uUBSFCRMmMH78eC5evMjSpUt5+OGHq3QOBw4c4Msvv0Sn0zF16lR0uuKXIJdm8+bN/PDDD1it1kLP5ebmsnbtWkJCQqqtAkeI2kjClKtkZmby6KOP8ueff2IwGHj88ccZPnw4LVq0wGq1smvXLt577z327dvHY489xvTp0wt86pQ/sc7NzS0xwbbZbEWeV5TSqlyEqA6V0TuhImw5dgwGQ6lBZpnoFHQGA+H3jOT8mv/SbuIEjIGB+Oj1BDVpUuBQh68vUZMmEj//MyIff1SCFCHqmaysLGbMmMG+ffuAvF0xpk2bRuPGjat5ZqKuUlWVn0/+rumYP538nfu7DKt1y1PHjx/PhAkTSj1u9OjR7Nixw6OxBw4cSFRUFHFxcSxYsIB7770XL6+qadbrcDh49dVXcTgc3H///RXa+fONN95g8eLFQF7Fzd///nfat28PwOHDh1mwYAEbN27kjTfe4OjRo+5lRUKIkpU/3qyj3n//fffuOm+++SbPPfccERERmEwm/P39ufHGG1m6dClt27bFbrczZcoULl686D7f19fX/diTprH5zxNC1ByBPbrTYfL/wxgYiE5fdHM+vcmET0QE0a/+U4IUIeqZy5cv88orr7iDlA4dOjBjxgwJUkSlSs1K43LOFU3HvJxzhdTsNE3HrO0URWH8+PFAXmX6kiVLquy1Fy1aRFxcHEFBQTz77LPlHmfz5s3uIOXuu+/m/fffp2vXrnh5eeHl5UW3bt2YPXs2w4fnVSV9/fXXbNq0SZP3IERdJ2FKPk6nk9WrVwMQERHBnXfeWeRxPj4+PPbYYwBYrVb32mjIayrrcuHChRJfz9WsNjAwUPqlCFFD6c1mjH4Nig1S3MeZTBj9/SRIEaKeOX36NKdOnQKgZ8+evP766/j5+VXvpESdd/ryucoZN71yxq3NbrrpJjp16gTAggULPGpkWxHz588HoE+fPmzdupU1a9YU+nLNJSsry/29Xbt2FRhnxYoVQF4w9PTTTxf7evkDm5UrV2r9doSok2SZTz6pqamkp6cD0LFjxxKPzb/d4smTJ92P82+HfOLEiWK3FnM4HO793CMjI8s9ZyGEEPWPas9FMWizjXBNofcNqJFjlaZz584899xz7N69m6eeegqDQS6tROWzOWylH1SDxq3tJkyYwOOPP05qaipffvkl48aNq/TXdLUD+OGHH/jhhx9KPDYtLY3nnnsOyAt/evbs6X7uxIkTADRu3LjEnUZDQkIICgoiJSWlwL2NEKJ4mv/FP3DgAL/++ivHjx/HYrGU2lgV8pJS197m1Umf75Pn0hpq2u129+P8F049evRwP961axdDhgwp8vzY2Fh3mpz/HCGEEPVPrt2J3VF6I0m9TkHvtJF1fBc+bXthV4zu3aauZtDrMBpqRwGqotMTMmKS5mNWltzc3AK9ma677jquu+66Sns9Ia5m0ldOFWRljVvbDRgwgK5du7Jv3z4++eSTcu+qUx1c9yn5712K4zpGQmEhykazn5Rz584xefLkQqVlpVFVtcY0ugoICKBBgwZkZGSwd+9eHA5HgYAlv/zvMzQ01P04MjKSiIgITp48ydq1a5k8eXKRzWVXrVrlfnzzzTdr+C6EEELUNnaHk38t2kl6RvEfQPTo0IQR17UkafNi0uO207jnKfz7DGPJ5pMcPnWpwLEBDcy88FCv2hOmGIwo1I5Km507dzJ37lymTZtGy5Ytq3s6op5q1bB55YwbUDnj1gUTJkzg0UcfJS0tzd2DpDKV5Z7qxhtvJDExkRYtWrBly5YijwkNDeXYsWOkp6cTHx9fbEW86xhAdiEToow0ucqyWCw8+OCD7Nq1C1VVPfqqSXQ6Hf379wfy+p18/PHHRR53/vx55s6dC+RV1Vx//fUFnn/kkUeAvJK7//znP4XOP3DgAMuXLwfgmmuuqVB3biGEEHVDekYOlyzWIr/ahwUy/NqWpGxaRNrB31GdKik7NnD599Xcd2MEoU0aFDi+pFCmtnE4nFgybVhtpX+qWtm2bNnC9OnTSUlJYdq0aeTm5lb3lEQ91dgnkIZmbXvzNDT70dg7UNMx65KYmBi6d+8OwGeffVbj7mOKM2DAAPfjGTNmFPl7y2azMX36dPc/33DDDVUxNSFqPU0qUxYsWMC5c+dQFIXQ0FAef/xx+vTpQ0hISK1rrPrUU0+xZcsWsrOzee+994iNjWXYsGG0aNGC7Oxsdu/ezWeffcalS3mfAg4fPrxQwjtixAhWrFjB3r17+fzzz0lJSeGuu+7C19eXXbt2MWfOHPe2yS+//HJ1vE0h6qwGDUreZryqxxGiovp2bsaDA1uTunkR6bHbCzyXunMDAOOGDmP+D7D/eEp1TLHSOBxOLlmszFr6Jzdc05KY7i3wMlVP+fmqVav47LPPADCZTIwbN06bbdiFKAdFUegf0ZfvDm/QbMwBEX1rTLV4TfX0008zduxYdwVHSZKTk9m6dWuh7wFkZmYWavIaExNDcHCwdpP9nxEjRvD5559z8uRJtm7dysiRI3nwwQcLbI28aNEijh49CuT1f3Tt7COEKJkmVySusrJmzZrxzTffEBBQdY3ftBYZGckHH3zA888/T3p6Ops2bSp2e7DBgwczbdq0Qt83GAx8+OGHjBs3jri4uCIbR/n4+PD222+7u4MLISpOp9dx90PXaDqeENWppCDFpa4GKq4g5d9LdnM+JZMv1x8CqPJARVVVFi5c6L7x8fX15ZVXXinQiF6I6jAoMoY1RzbhUEvvt1QavaLj5sgYDWZVt/Xr14+ePXuWaQnOiRMneOmll4p8Lj09vdBzixYtqpQwxWQyMX/+fJ588kmOHj3KoUOH+Oc//1nkse3atWPu3Lm17sNwIaqLJlcjiYmJKIrCfffdV6uDFJeYmBj++9//snz5crZu3crx48e5cuUKJpOJJk2a0KVLF4YPH06/fv2KHSMoKIhly5axbNky1qxZw8mTJ7FarTRt2pSYmBjGjBkja62F0JjBoINa0iNCiNKUJUhxuTpQOXsxoyqmWGmuDlIAVJUqD1QcDgezZ89m8+bNAAQGBjJ16lQiIiIq/bWFKE2TBkHcHX0byw5+X+GxRnYaSpMGQRrMqu6bMGECY8aMqe5peKRly5asWLGCH374gQ0bNhAXF0daWhqQ93stKiqKQYMGMXToUAlShPCAomqw4K93795cuXKFWbNmccstt2gxL+GhrKws9zrOPXv24OPjU80zEkIIUVbZOXZenvMrlyxWwLMgJb/GvQbh32cYS7ec5KEhUXiba9+ODEUFKfkpCjwwuGOlByo5OTm8/fbb7NixA8irvp02bRpNmzattNcUwlN2p4M3fnqPuORj5R4jKrgtrwx4BkMl7oAlhBB1kSYf4boqLC5fvqzFcEIIIUS9Vd4gBfIqVCzbV3P/TRHodLWv90FpQQr8VaGydU9ipTalzcrK4tSpU0DeEuC33npLghRR4xh0eibHPElUcNtynR8V3JbJMU9KkCKEEOWgSZgyZMgQVFXl119/1WI4IYQQol6qSJDi4gpUDGrt2mmmLEGKS1UEKoGBgbz22mtcd911TJ8+vU4sYxZ1k7fRi1cGPMM9nW5Hr5Tt0l6v6Li38x28MuAZvI1elTxDIYSomzRZ5pOdnc3dd9/NyZMnmTt3rnt7YVF1ZJmPENqz2nNQUDAbZP2wKJ7FYiEhIQGHw1Gu841GI+ERbVi+5Rh3X9+KlM2LSY/9vUJz0ikKLa4dgv/f7uD0uSSuXLlSrnH0ej1hYWH4+/tXaD6l8SRIyU/rJT9ZWVny91PUahczUtgYv5WfTv7O5ZzCP/cNzX4MiOjLzZEx0iNFCCEqSJMwBeDMmTM8/vjjnD17lieffJIHHngAPz8/LYYWZSBhihDasTvsZOZm803sGsIDQrk2rBdeBtkqWRQtLi6Oixcvlvt8s9lM9+7X4LDlkLxpUYWDFACdTsHf14RP5xto2OdOjsSf5NKlS+Uaq0mTJkRFRVV4TsUpb5DiolWgEh8fz7Rp0xg7diw33HBDuccRoiZQVZXU7DROp5/D5rBh0ptoFdCcxt6Bsv2xEEJoxKMw5aGHHirx+YyMDOLi4lAUBb1eT3h4OIGBpf/SVhSFhQsXlnUaoggSpgihjRy7jYT0syzc8w0pWXk3n12aduT+LsNoYPLFqK99DT1F5apoZUpQUBDNghuTuPZTLmkQpEBeZYq/rwmHw4F/t4E07DeMM+cueFyhUtmVKRUNUlwqGqjs27ePN998k+zsbPR6PR9++CHNmzcv93yEEEIIUfd5dMWxY8eOMgUjAHa7nfj4+FLHVFVVEnIhRLVzOp3kOGxsjv+VtUd/xKH+dWO8P+kQZy6f46FudxPZKEyW/YgC/P396dy5c7nPd+Zkk7plMemH/kBBo7+HigIoGAwGsg7+hF6vJ6LfcHSmmtMbQasgBSq2bfK2bdt45513sNvtKIrC3//+dwlShBBCCFEqjz++8WRVkEYriIQQolLZHLlctlpYtHcFR1NOFHlMWvZlPtj+GTe3ieHWtjdgNpjQlbHRnxDFcQUpmUd2lPkcNd//p4zxy5V9mwEIqCGBipZBikt5ApV169Yxd+5cVFXFYDAwceJErr/+ek3mI4QQQoi6zaMw5fDhw5U1DyGEqBZWew5xF4+xZN8qrthKvqlzqk7WH/uZ46mnGNP9bgK9AzDpjVU0U1EnKQo6o6eVTmq+LEWFMlazKB6/TuWojCDFpayBiqqqLFu2jCVLlgDg5eXFSy+95F4uK4QQQghRGln8L4SolxxOB1Z7Dt8e2sAvp/5ApeyVdPGXEpj5yxzu63InXZp2lOa0otx0Ji8Cr78XVQXLzi1lPEv5X4jyv8dl4N/zVhr2uq3aq1LKGqQ4nWqZetDo9Xp0uoL/G5QWqDidTubPn8+aNWsA8PPzY8qUKbRr187TtyOEEEKIekxq1IUQ9VJmbjb/+W0+P5/a7lGQ4pKVm80nu7/i+8ObsNltlTBDUV/kBSr3ENi1bMtL8rqhuP6vdDUlSAHIdTj54dcTpVakOJwOnE5nqV8OZ9GBi6rCN1uOkn4lB6dTveo5ldTUVACCg4N56623JEgRQgghhMc0qUyZPXs2ALfddhsRERFlPu/06dN89913AIwfP16LqQghRJmYdAay7dYKj+NwOsoRxQhRkF1notH194IKl/b+rNm4NSlIATAb9Yy8qR3J6dnEnkgt9ji9Tk9ZfrD0On2R3zcZ9Tx1dzcC/b0KVa7o9Xqef/555s+fzz333ENQUJBH70EIIYQQAjSqTJk9ezYffvghJ04U3bixOAkJCe5zhRCiKimKjs4hHSo8To/mnWV3H1FhTqfKsp9OEXj9PTTq1l+TMRv2qllBCuTt+OfrbeTJu7oS3bpxscfpdApGo6HUr6uDEsgLUiaM7Eb78EaYjXlhy5UrV7Db7X8dYzLx1FNPSZAihBBCiHKTnilCVDNnbg5qrg3FaC5HI0rhKYvFQkJCAo0bN6ZbSBQbj/5S7rECvQNo4RfC4cOHsVqLrnLR6/WEhYXh7+9f7tcR9cOZpCt8/XMC9wy4FxRI21f+/zab9B2Kfw0LUlzyBypzVuwrsULFU0UFKRcvXnT3RHnmmWfQ6WSFsxBCCCEqrlrDFKfTCeTdbAhR36hOJ2qulcs71pBx6DcaXX8v3q271cibn7rk7NmzpKamYrVa6dg5igAvfy5lp5drrC5NO2C1WklKSirxOL1eT1RUVLleQ9QPBr2OFx7qBeRVZTQf+ADeJgMZsb96PFbDXrfi3+s2VH3NDWcrI1ApKkg5ffo0U6ZMITU1lcTERHr06EH//tpU/gghhBCifqvWMCUxMREAX1/f6pyGEFXOmWvDcSWVlI2fYUvKWx6Xsn4Bvh37ERgzEp3JG0UvhWOVITQ0FIfDgcPhwG6307VZFD+f3F6usa5p0QVbVg4BAQHFHqPX6wkNDS3vdEU9YTToMBryV0x406j/vSgKHgUqNa1HSkm0DFSKClIOHTrE66+/TkZGBgB33nknMTExmsxdCCGEEELTuzVFKdsWjdnZ2cTGxrJw4UIURaF169ZaTkOIGs1ps5J9Yi+XflqCMyerwHOZh7aRk3SSoEFjMTZugc4oW+5qzd/fn86dOwN5u3r0atmNbWd3ez6OuQGRjcPwNnrRrFkzracphHvbZChboFKbghQXLQKVooKUXbt2MXPmTGy2vJ22xowZw4gRI8p8nSKEEEIIURqPw5TiGsaqqspTTz1VrkncdNNN5TpPiNpEddhx2rJJ27qczEPbij3OnnaeC9+8TUDfYTTo3B/FaJYbgEqiKAoRgS3xMzfgSk6GR+d2CmmPTv69iEpW1kClNgYpLhUJVIoKUn788Ufee+89nE4nOp2OJ598kkGDBlXW9IUQQghRT5WrC5uqqgW+ivt+Wb569OjBQw89pNkbEqImcubmYEs+TdLyf5UYpLiojlzSfl1Oyn8/xpGRhmrPrYJZ1k+KotA5pL3H53Vv1glTDe5JIeoOV6DSIPq6Ip+vzUGKS1l3+cmvqCDl22+/ZdasWTidToxGIy+++KIEKUIIIYSoFB5XprRo0YJevXoV+N7OnTtRFIU2bdqU2DsAQKfT4ePjQ2hoKH379mXAgAHSWV/UWaqqoubmkHHgZ9J/X43q8CwUyT51gKRlb9J44BjMLdrJsp9KYNab6NYsmm2ny77Ux9foQ5tG4VIxJKpMcRUqdSFIcfGkQqWoIAX+amjv4+PDK6+8QqdOnSp93kIIIYSonxQ1f2lJOXXo0AFFUZg9e7Ys2akmWVlZdO/eHYA9e/bg4+NTzTMSkNcf5creTaT/vrpiAyk6QkY8h7lFOxTF8/DRkZMDgN4sYUxRsmzZvLLpbTJzs0o/GOgd2o0Hug7HyyD/e1ZUrsOOw+nQdEy9To+xjjZwdtqspP3yFRmxv9apICU/VVXJzM4tNlApLkhxWbVqFd26dSMiIqIqpiuEEEKIekqTq01XpUpgYKAWwwlRZyhGM6aQil/QKwYjxsYtPA5SVKcTh9XK+e/XoBgNNLv1FnRmM4pUgxWg1+l59tpHyS1j5ZCf2Q+zLPHRhMPpYNa2+Vy2XtFkvIZefjzbb1ydDVNcFSperaLwDu9S54IUKLlC5eogJScnh8zMTBo1auQ+Zvjw4dUxbSFEFfrjjz/cbQLGjx/PhAkTSj1n9OjR7NixA4AjR44UeO6DDz5g9uzZ7n+eMWMGI0aMKHG8qKgoHA4Hw4cPZ+bMmYWet9lsxMXFceDAAfbv38/+/ftJSEhAVVVatGjBli1bPHqfpSltzqqqsn79elavXs3BgwdJT0/H39+fNm3acOeddzJ8+HBZLSCEhzS52ly8eLEWwwhRa1ksFhISEnA4Cn7C7u3tTeuWYTiN3jiyLOUe36dVJ+xOOLhvH2UpJjObzYQ2bYouM4tTny4k80Te9stXDh8l4pGHMTUKRGeSMMDFbDDRsmHz6p5GvXXZeoU06+XqnkatoTN54dO6O4rBWN1TqTRFBSpXBykZGRm88cYbXLlyhRkzZuDv71/d09ac6nCg6AtX3wghKtecOXO44447MBjKf6s0ZcoUVq5cqeGsys9isTBx4kR+++23At9PTU0lNTWVP/74g5UrVzJ37tw6+btUiMpSNz+6E6KKnT17ltTUwuXo6enptGjaBO/wzlgOlr61aXF8WncjJS2dtLS0Uo8NCQkhPDQUy559nFj8BWq21X0xcOXoMeLemEHY6PsJ6NoFvVfd+1RbiPqgLgcpLvkDlQXfHuDGnq3cQcqlS5eYOnUqp06dAmDq0qMwAAAgAElEQVTZsmWMGzeueidcCRzZ2ejMZnTGuv/vW4ia5MyZM6xatYqRI0eWe4z8H375+voSHR3NyZMnSU5OLtd4b775Jp07dy72+aZNmxb5fafTyTPPPMO2bXkbIHTp0oWHH36Y8PBwLBYLGzZs4KuvvmLXrl08/fTTfPbZZ9ITTogykjBFCA2EhobicDgKVaYAZGTn4Nv2GjLifivizNIpBiM+rbtyKTmtxAbPer2e5iFN8TUZObNkKclbf/vf9wv+mNszMoifO4/g62NoOeoudN7e6OSTTyFEDeQKVB4f0RWdTsFs1HPu3DmmTJnChQsXAOjbty9jxoyp5plqz2HNIXnrrwT3v17CFOExVVWxpaSQmXAaZ44NndmEb1grTEFBcqNcisDAQNLS0pg7dy7Dhg3DWM6fv+uvv57evXvTuXNnIiMj0el0jB49utxhSmhoKO3atfP4vHXr1rmDlJiYGObOnVvgPfXt25euXbsyefJkfv/9d3744Qduv/32cs1RiPpG8zAlKyuLzZs3s2/fPi5cuEBGRkaRN5j5KYrCwoULtZ6KEFXG39+/xE8LHAF+mP0CcFozPR7bK6wTBrM3rVuX3JPIkZND9tlEji74FOv5JAylfHKd/MtWMuLjaT3u73g1ayrNaYWoJDn2vAbQZmlYXC6KouBtzrtciY+PZ+rUqVy+nLcsbPDgwfzjH/9w7+JTl9ivXCFx1XfoTGaCr79OAhVRJtYLF0hat4GLW34kN73w8kljQEOa3HgDTW8ZhFdISDXMsOZ75JFH+Pe//01iYiIrV67knnvuKdc4Q4YM0Xhm5bN69V+bILz66qtFhkPDhg3j66+/Zvfu3SxYsEDCFCHKSNMwZdGiRbz//vtkZpb9hlFVVUnIRZ2n6PR4hUWTdWSHx+f6tO6GUkIzTVVVcVpzuPjTT5xd+S1qbtm3X85OPMehN98idOQIgmOuk2U/Qmgsx57Dpvhf8TX50LdlDwlUKmD//v1Mnz6d7OxsAO655x7uv//+OnkN4bDmcP6/63DabCStW0/jvn+TMEWUyGm3k7hiFWeWLUct4UPM3PTLJK5czblvv6flvaNoMWIYugr0BamLhg4dyurVq4mPj+ejjz5i+PDhmGpxn7nY2FgAwsPDadWqVbHHxcTEsHv3bg4fPszp06dLPFYIkUezls2zZs1ixowZZGRkoKpqiV8uGuzKLEStoBhN+ET28PxEnR7v1l1LDFOcVivxH8/nzLJvPApS3OfbbJz+8itOfroQx/9uUoQQFZdjz2Fj/K98d3gjX+3/jt/P/OmuUhGe2b59O1OnTnUHKY899hgPPPBAnQxSIK8qJeXXvLL8nOQUUn//A2c5fr+L+sGelU3slNc4veSrEoOU/FSHg9NfLiV2ymvYs+Rvf346nY7x48cDcO7cOZYvX17NM6qY9PR0gAK7nhUlKCjI/Xj37t2VOich6gpNwpTDhw/z8ccfA9C6dWsWLVrE/v37gbzy3A8//JA9e/awZs0aXnzxRXeDpOHDh7N//34OHTqkxTSEqLEURYdXaAcUD7cx9WrRFp3Zp7TBcVitFZhdHkd2NtTRGxMhqporSPn+8EYAVFQJVCogODgYo9GIwWBg0qRJDB06tLqnVGnyV6W4JK1bL2GKKJLTbufQ9BlYDsaW63zLwVgOTZ+B027XeGa12y233ELbtm0BmDdvHrZ8P4/VZdasWQwYMIBOnTrRu3dvRowYwdtvv01iYmKJ5/n45F1HZmRklHicxfLXrpPHjx+v+ISFqAc0qev76quv8gYzGPjkk09o1qxZoWO8vb2JjIwkMjKSkSNH8vTTT7N69WoyMjL44IMPtJiGEDWaojfg3SqarONlT/u9I7qh6EruBaDoDQR278qVw0cqNL/A7t1QpNS3xilu2+26wGg0Et4mgly7nVyNbhRzDXYcTgexsbGajekJX19fmrVszuYTv/Ft3PpCzy/evQKHw0Hv5t1IPH0WqwZBqKf0ej1hYWG1avvLyMhIXn75ZZxOJ927d6/u6VSq/FUpLq7qFOmdIq6WuHJ1uYMUF8vBWBJXrqblqLs1mlXVSk1N5ejRo6Uel5WVVeYxXdUpzzzzDElJSSxbtozRo0dXZJoVtmfPHvfjy5cvc/nyZWJjY1m4cCEvvPACDz30UJHntW7dmr179xIfH8+lS5eKrVDZtWuX+3FSUpK2kxeijtLkzmnXrl0oisKtt95aZJByNV9fXz744AMGDx7Mpk2bWL9+PYMHD9ZiKkLUWIrBhE+ba8g+XbaLHkXR4RPZvcQlPhaLhfT0dIK6dcX+5VJwOss3N70evy6dSDhzxt3Y8WqVdQOWk+sgzWKlYQMzXiZ9nS3bL6/itt2uC8xm8/+WfzpxquX7b/dqqupEVVUsFgs5OVVbAeLv70/TFk3ZFP8rq+PWFXvcF3tWoqoqvUK7cvTQEY/6jGlFr9cTFRVV5a9bVk6nk9OnTxMeHu7+XteuXatvQlWkqKoUF+mdIq5mvXCBM199rclYZ776muD+MbWyKe3SpUtZunSp5uMOHjyY9u3bc+TIEebNm8eoUaMwV0Oz/iZNmjB48GB69OhBaGgoiqJw9uxZNm7cyH//+19yc3OZPn06Op2OBx98sND5N954I3v37sXhcPD+++8zderUQsfs2bOHn3/+2f3P1fF3SYjaSJMwxZVeFndhVlRpnI+PDyNGjODjjz9m9erVEqaIOk/R6fBu3ZXQ8H+V/ZwSghTIu9G+dOkSTbp1w7d1a64cPVauuTXs2A7FbObsoUMlVkBofQNmzbHzy95Elm8+SvtWgYy9PRp/XxNGQ93bmaO8Stp2u7YzGo0oioKi6NAp2rTwUhQdiqLg7+9fpZUp+StSvju0odT3s3TftyiKQu/oqq9Q0ev1hIaGVtnreSo3N5f33nuP7du38/rrr9OxY8fqnlKVKaoqxUWqU8TVktZtKHOPlNKoDgdJ6zYQPqZ6qy9qEkVRmDBhAuPHj+fixYssXbqUhx9+uErn0LlzZ3788UcMV1UOd+7cmVtvvZU777yTp556itzcXP71r39x8803E3JVIHbfffexePFikpOTWbp0KTk5OTzyyCOEh4djsVjYuHEj77zzDjqdzn2tUR1Vk0LURpqEKa4fuKt/eL29vbFarcWu0XPtlX748GEtpiFEjaczavuJhutGO9tqpXGPbmSWc41r42u6k2W14ufnV+wxWt6A2e1OrmTZWLQ2jj+PXATgQHwKry3YziO3d6J9eCBeJllyBKVvu13bWXNzMBoMRW7VWBRfow8mffHHNvTyQ6/TEx0drdUUy8TVI2XtsS1lfi/LY9eg1+vp20Z2+XHJzs5m5syZ7nL2zz//nJkzZ9aLirX8VSmqquJwFO5hcf6/66Q6RQB5mzhc3PKjpmNe3PIjYQ89WOt+3saPH8+ECRNKPW706NHs2OHZrooDBw4kKiqKuLg4FixYwL333otXFe586Op3Upz+/fvz1FNP8e6775KTk8OKFSt48sknCxzj7+/PnDlzeOyxx0hLS2PlypWsXLmywDE6nY4pU6YwZcqUMr2uECKPJncrvr6+WCwW7Fc1r/L398dqtXL69Okiz3OVkNXVEnYhKlv+G23v3r05t+o78HCXLEWvp3GvnpgbN6ZRcHBlTLMAq81O/JnLfPL9AVIvF/zkIz0jh1lf7WbQ38K58/pIvM0GdLradVEnKo+v0Yep/Z9GV8KKIAUFg92Jw162T9UUg77CN6ZXN5stK1dTWkC2TSZv2eJrr73m7n3Qvn17/vnPf9a6G7vyyl+V4nA4cDoL/y7PSrog1SkCAFtKCrnpRS/LLa/c9MvYUlIxBweVfnA9oSgK48eP58knnyQ5OZklS5bwyCOPVPe0Chg1ahTvvfceqqoW6HuSX5cuXfj222+ZM2cO69evJy0tDch7fz179mTixImEh4e7w5SGDRtW2fyFqM00CVPCwsI4cOAAFy5cKPD9Nm3acOHCBbZt28akSZMKnefadsvb21uLaQhRrxkDGuIbHkbmyVMenecb2RqDr2/lTCofp1PFarOz5reT/HfbSRxF3ChAXha0fvspjp1O49FhnQkO8MZklGU/Akx6IzonxM58i5z/bfV4NR06Gpi9gdJvwI0NA+jwwiSowE1peYMUFwlU8iQnJ/Pqq6+6d6Xo0aMHL774YpV+Alydru6VotfrgcK/I/V6vfROEQBkJhT9QWXFx02QMOUqN910E506deLgwYPu6pSaVLnRuHFjAgICSEtLK3Qvll9ISAjTpk1j6tSpJCcnk5WVRVBQEA0aNADgzz//dB8bGRlZ6fMWoi7QZJF6p06dUFW10BbH1157LQBxcXF88803BZ7btGkT33//PYqi1Kv10EJUFkWnI6BrF4/PC+zWBUVfuWGFLdfBhbQs/rNkNz/8eqLYICW/E+cu8/on2/kjNglrjmzZKP6Sk55OTmpq0V+XUrFdSivTV+7logOZMs+jgkGKS33fNvn06dNMnjzZHaT079+fV155pd4EKVC4V4qiKBgMxkJfiqK4e6fIVsn1mzOncrbqLar5scC9jCg1NZUvv/yymmdTmCcVfIqi0KRJE8LDw91BCuTdr7l06eL59aQQ9ZEmYUq/fv0A+O2333Dm201k2LBh7h4M//d//8fIkSOZNGkSI0eOZMKECe5jR40apcU0hKjXdCYTjXr39OwkRSGw5zWV+gmn1WbnzyMXef2T7Rw749nNa3aOnQXfHuDzNbFYMnOw27XZ8UWIitIqSHGpr4HKiRMnePHFF0lJSQHg9ttv59lnny3UbLEuK2kHn+IkrVsvYUo9pzObKmdcU+WMW9sNGDDAvaPYJ598UqN2u7l06ZJ72U5wBZZrr1+/3j1Gjx49NJmbEHWdJmFKTEwMzZs3x2AwsG3bX5+sNGrUiGnTpqEoCqqqcvDgQdauXcvBgwdR/9fX4fbbb2fIkCFaTEOIes/UqBE+rVqW+XjfiHCM/pW7LjY5LZu5K/aRmV3+C//fD5xn1U/HsZdz62chtKR1kOJSHwOVkJAQ98X/6NGjefTRR9HptNnZqbYoaQef4kh1ivANa1VJ44ZVyrh1gas6JS0tjcWLF1fzbP6yfPly931Vz54efqj2Pzt27HA35x05cmS9CrSFqAhNflLMZjNbtmwp8rkhQ4YQHBzMBx98wJ9//uluUhsWFsbo0aOL3A9dCFFOikLrRx/BXsZPTAwNGqBUcoPXoABvggK8SUnPrtA40a2DpHeKqHaVFaS41LceKr6+vkydOpV9+/YxYMCA6p5OlStPVYqL9E6p30xBQRgDGmrahNYY0BBTUGPNxqtrYmJi6N69O3v27OGzzz5zBxiVxWKxcPjwYXr37l3sMb/88gsffPABACaTibvuuqvI4y5cuFBo11WXEydO8NxzzwHQokULxo0bV8GZC1F/VEns2KtXLxYtWkRubi7p6el4e3sXWKMnhNCG3mz2qDKlKigKdGsbzKadBZvlOZ0qDoejyHP0en2BXXx8zAY6hAWiqye7eoiaxWKxcPr0aSLatObP8wdYeWBtpb/m4t0raGj2o03DMOKPxaPX6wkLC8Pf37/SX7uyxcbGFti6OjAwsF4GKVC+qhQXV3WK7OxTPymKQpMbbyBx5WrNxmxy4w31Zves8nr66acZO3Ys6cU0Qc8vOTmZrVu3Fvoe5O1oevX2xDExMQWW6VgsFkaPHk3Hjh0ZOHAg0dHRNG7cGJ1Ox9mzZ9m4cSNr1651t02YNGkSzZo1K3IuU6ZMIT09nUGDBhEdHY2fnx8pKSn8+uuvLFu2DKvViq+vL7NmzapRzXWFqOmqtIbLaDRWaC2fEKL28TIZ6B3dtFCY4nA6CvRYKkABne6vX0+d2gRhMNSv0v/6pKGXX5mPU1DQoUOnFP3fQ2XcCJw9e5aUlBS8vLzo0rwj7YMjOXTxmOavk1/PFl1oExhOYsJZ90W7Xq8nKiqqUl+3MqmqyuLFi/nmm294+OGHGTFiRHVPqVpVpCrFRapT6remtwzi3LffoxbzwYQnFL2eprcM0mBWdVu/fv3o2bNnsVsQ53fixAleeumlIp9LT08v9NyiRYuKvE86dOhQoU0+8jOZTLzwwguMHj262GNUVWXPnj3s2bOnyOdbtmzJO++84+4LI4QoG1kQJ4SodK2a+tPI34tLFqv7e3qdvqidP/96Lp8e7UMwGWSJT12k1+l5tl/ZS4oNdicNzN6YzCVt561toBIaGorD4SAjIwPvNG/+0Xs083Z+WWmByjUtOjOm+0guXUzFarUSEBCAXq8nNDS0Ul6vKjgcDubMmcPGjXnLo7799lsGDRpUr6tUK1KV4iLVKfWbV0gILe8dxekvl1Z4rJb3jsKrmGUgoqAJEyYwZsyYSn+dJk2a8N5777F37172799PUlISaWlp5Obm4u/vT+vWrenTpw+jRo2iSZMmJY712GOPERYWxq5du7hw4QKXL18mICCAiIgIbrnlFu666656tYOaEFpR1EpY8Gez2di3bx/x8fFYLBZsNhvjx4/X+mVEPllZWXTv3h2APXv2SImeqFGsNjtLNxzhp91nPD7Xy6TnnWf64+cjOwzUFBaLhYSEhGKXaXkib0lX2aqODAYDEaGh7H/p/7BdulTCkWX7s2Zq1IguM97g2KlT5HrQyDM4OJgGjfwrJVDJH6Rcyvcea/MyH5vNxjvvvMP27dsBaNq0KdOmTSu2HL0+cFhzyDh+HMuhwxUey9SoEY37/g2D/N2vl5x2O7FTXsNyMLbcY/h3iiZ62qvopOmoEEJ4RNPfmjk5OXz44YcsXbqUjIyMAs9dHaa8/fbbbNq0iaZNm7Jw4UItpyGEqGHMRj29OoaUK0yJimiMUZb41Chnz54lNTW1yl/XbDYT3qIFTqez+CViHnA6VVRVxWKxkJNT9t1z0tPTCQ0N5bGe9/PRzi80C1R6tujCQ93u5uypMyQlJRV6vjYu88nMzGT69OkcPHgQgIiICKZOnUpgYGA1z6x6KQY9Ddq1pUG7ttqMJ30u6i2dwUDHf77EoekzyhWo+HeKpuM/X5IgRQghykGz35wpKSmMHTuW48ePF+puXdQf+UGDBvHJJ59w+vRp9u/fT5cuXbSaihCihlEUhdYtGuLva8KS6Vl/gB4dQiRMqWFcy160qEzxhNFoRFEUdDqlQIPi8tLpFBRFwd/f36PKFEDzJT9FLe3JrzYu80lLS2Pq1KmcPHkSgE6dOvHPf/4TX9+SlmjVD3LjKrRk8PEmetqrJK5czZmvvi5TDxVFr6flvaNoMWKY/PcohBDlpMlvT1VVeeqppzh2LO9ismfPntxxxx1cuHCBDz/8sMhzunbtSmhoKImJifz8888SpghRx+l0Cl3bBrN1b2KZzzEadHRpE4S+jMtARNXw9/enc+fO1fLajmwrBoMBp6Hi/SEMBgN6nb7AzjKecqpOnugzmvk7l3A4Jb5cY3Rv1omHut+Fj9GbBq18adWqVbnnU1NYLBYmT57srrDp06cPzz//PCaTLNcTojLoDAZajrqb4P4xJK3bwMUtPxa5bbIxoCFNbryBprcMkh4pQghRQZqEKT/88AP79u1DURQef/xxJk6cCMCmTZtKPK9v374sX76cvXv3ajENIUQNZjbquaZDiEdhSsfwRphN0nhW1Fw6RYeP0Ztxve4vV6CSP0ipS/z8/OjWrRvr1q1j0KBBPPHEE+j18rMsRGXzCgkhfMxowh56EFtKKpkJCThtNnQmE75hYZiCGsuyMCGE0IgmYcratWsBiI6OdgcpZdGuXTsgb+swIUTdpigK7VoFEtmiITm5ZVse0qdTcwx6qUoRNZsrUHnyb2PYeXYvF7PK1k+muV9TejTvhElf93ZhcX24Eh0dzfXXXy83b0JUMUVRMAcHYQ4Oqu6pCCFEnaVJmBIbG4uiKNx2220endeoUSMgb121EKLuMxp0vPBQrzIfr1MUCVNEraBTdDhtCs1tEQSqzUttkKvX6TBlmjhzvOi/f4GNffFr6IXRWHuqOfbu3UuHDh3c22vq9Xr69+9fzbMSQgghhKgcmoQprjCkefPmHp3n2g5Ti10ZhBA1n6kW3RgK4Sm9QeHQgSQOHzhPVm42dqe9yOOMOiPeRq9iqzVMJj1jnuxXq4KUDRs2MGfOHLp168Yrr7yCQRpaCiGEEKKO0+QjXx8fHwCys7M9Oi85ORmAhg0bajENIYQQotoYjXr+dl0Een3esh+DrnCgUFqQAtCpRwu8vGtHGKGqKl9//TWzZ8/G6XQSGxtLQkJCdU9LCCGEEKLSaRKmNGvWDIAjR454dN6uXbsAiIiI0GIaQgghRLVRFAX/QC/aRoWgKEqhQKUsQYrJpOeaPmEYTTU/THE6nSxYsIAvvvgCyGs6+8YbbxAZGVnNMxNCCCGEqHyahCl9+vRBVVXWrFlDTk5Omc5JSEhgy5YtKIpC3759tZiGEEIIUa1c1Sk6nVIgUClLkAK1pyolNzeXWbNm8f333wMQFBTEzJkzad++fTXPTAghhBCiamhyxXb33XezaNEikpOTmTJlCjNnzizx+JSUFJ5++mnsdjtms5mRI0dqMQ0hhBB1mNNmA50OY0CgJuMZGwZoMk5++atTjhxMcgcqrudKUluqUqxWKzNmzGDPnj0AhIaGMm3aNIKDg6t5ZkIIIYQQVUeTK7Y2bdpw//3388UXX/Dtt99y8uRJxowZQ3p6uvuYc+fOcf78eX777TeWLFnC5cuXURSFJ554gqAg2bZNCCFE8Rw2G1mnEjA1bkyHyc+DRo3LFYP2TV5d1SnH4i7gdKpl3ha4NlSlqKrKa6+9xsGDBwFo164dr776Kv7+/tU8MyGEEEKIqqXZVdtLL71EUlISmzZtYv/+/UyaNAn465O4m266yX2sqqoADB8+nH/84x9aTUEIIUQd5LDZyDxxkmPvzcYU1Jh2zz6NqWFDFH3N3O3m6uqUsqgtVSmKojB06FBiY2Pp1q0bL730knsrZCGEEEKI+kRRXcmGRhYuXMi8efNITU0t9pjAwEDGjx/PAw88oOVL12tZWVl0794dgD179rh3WBJCiNosf5Di+N+Ocd4tQ2t8oKKqKqkXM/li3nacztL/zPbo04prb4is8WGKy969e4mOjsZoNFb3VIQQQgghqoXmYQqAzWZj69at7Nq1i8TERDIyMvDx8SEkJIRevXrRv39/vL29tX7Zek3CFCFEXVNUkOJS0wMVi8WCXm9i85pDHNp/vsRjTWYDY8dfy5WMSzRs2LDGLZk5cuQIXl5ehIWFVfdUhBBCCCFqjEoJU0TVkzBFCFGXlBSkuNTkQCUuLg6z2Yyvd2MWzdlWYnVKr2sjuObaUPbt20NwcDBRUVFVONOS7d69m5kzZ+Lr68u//vUvmjRpUt1TEkIIIYSoETTZGlkIIYTQSlmCFIDsM2c5Out9bJcvozocVTjD0oWGhmK1WvHzN9OhSzMUnVLkl9nbyDX9wrh8OY1GjRoRGhpa3VN3+/nnn3njjTfIyckhPT2dw4cPV/eUhBBCCCFqjNqxOFsIIUS9UNYgxcUVqNS0ChV/f3+io6NRVZV+/dty4khqkdUp3Xu3wtfXTEBgzVpC891337FgwQIAjEYjkyZNol+/ftU8KyGEEEKImkMqU4QQQtQIngYpLjW5QiX/zj5Xq4k7+KiqyuLFi91Bire3N1OnTpUgRQghhBDiKh5dweXf3lhLiqKwadOmShlbCCFEzVfeIMWlplaoABiNev52XQTH4i4UqE7p1KMFXt41J0hxOBzMnTuXDRs2ANCwYUOmTp1KZGRkNc9MCCGEEKLm8egqLjExEUVRKEvPWkVR3I9VVS30z8UdK4QQon6paJDiUlMDlfzVKUcOJgE1syrl008/dQcpTZs2ZerUqTRv3ryaZyWEEIX98ccfPPTQQwCMHz+eCRMmlHrO6NGj2bFjB5C3S1l+H3zwAbNnz3b/84wZMxgxYkSJ40VFReFwOBg+fDgzZ84s9LzNZiMuLo4DBw6wf/9+9u/fT0JCAqqq0qJFC7Zs2VLqnK+2a9cuVq9ezc6dO7l48SKKohAUFES7du3o27cvw4YNw9fXt9jzU1JSWLx4MZs3byYxMRG9Xk+rVq0YPHgwDz74YInnCiEK8+gqrrSLKrvdTnJyMqqqugMTf39/vL29yc7OxmKxuI9VFIXg4GAMhppzISmEEKJqaRWkuNTUQOXq6pSaVpUCMHToULZu3UpgYCBTpkyhUaNG1T0lIYSoFnPmzOGOO+6o0H3KlClTWLlypSbzyc7O5tVXX+W7774r9FxmZiYJCQls3LiRHj160LFjxyLH2L17NxMmTCA1NbXA92NjY4mNjeWbb77h448/pnXr1prMWYj6wKPfECUlqOfOnWPixIlcvHiRLl26MHbsWPr27UtAQID7mPT0dLZt28aiRYvYu3cvzZo147333qNp06blfwdCCCFqJa2DFJeaGKjkr045eTS5xlWlADRr1ow33niDRo0a0aBBg+qejhCiAlRVxZJu5WKShVybA6NJT5Om/vgHeElFeBmcOXOGVatWMXLkyHKPkb8S39fXl+joaE6ePElycrJH4+Tk5PDEE0/w+++/AzBw4EAGDx5MWFgYOp2OpKQkdu7cyfr164sdIzExkSeffJL09HSMRiOPPvooMTEx2O121q9fz5IlSzh9+jT/+Mc/WLFiBX5+fuV700LUM5pcyVmtVh5//HGOHz/O2LFjmTx5cpHHBQQEMGTIEIYMGcK//vUvPv30Ux577DGWL1+O2WzWYipCCCFqgcoKUlxqYqDiqk7x8zfXiKqUpKQkjh8/znXXXef+XqtWrapxRkKIikpLzWL37wns23mGzAxboed9G5jo2qsl1/QNI7CxTzXMsOYLDAwkLS2NuXPnMmzYMIxGY7nGuf766+nduzedO3cmMjISnU7H6NGjPU3OLOgAACAASURBVA5TPvjgA37//XdMJhOzZs1i4MCBBZ7v3LkzN998My+99BKOYpqw/+c//yE9PR2Ad999t8AYf/vb3wgLC+PNN98kISGBTz75hIkTJ3r4boWonzTZzWfJkiUcO3aMLl26FBukXO2FF16gS5cuHDt2jCVLlmgxDSFqFafTiVrEVqlC1HWeBimqqmK35xb4Kkvvrpq2y4+rOqXXtRHVXpVy4sQJXnjhBd555x13DwEhRO3lcDj5ZeNRPpz5I9t+jC8ySAHIzLCx7cd4Ppz5I79sPIbD4azimdZ8jzzyCJBXzVGRZTpDhgxhxIgRtG3bFp2ufLdcCQkJfPbZZwBMnDixUJCSn6IoRS5LunDhAmvXrgVgwIABRY7x0EMP0bZtWwC++OILbLai//sRQhSkSZiydu1aFEXhtttu8+i822+/HVVVWbNmjRbTEKLWyM11kHIhg8uXs7Hbq/8mT4gqpao4srJw2u1lOtzhcOB0qgW+ivv07WpOqxWnzVZjgkuTyYDRVL1VMgcPHuTll18mPT0dp9PJ8ePHq3U+QoiKybHa+eLj7fy07miBHcNK4nSq/LTuCF98vJ0ca9l+F9cXQ4cOde9i9tFHH1VrsLBs2TLsdjt+fn48+OCD5Rpjy5YtOJ15odmwYcOKPEZRFO68804Arly5wh9//FG+CQtRz2gSppw5cwaA4OBgj84LCgoqcL4Q9YEtx87BPxP56rOdLF2wg5PHUrHZ5EJG1B96sxn/6CjaPPEYShnKp/V6PTqdUuBLX4ZlO+bgINpNmog5KAidsfqX1bgYDJr86S2X7du3M2XKFLKysgD4+9//zv33319t8xFCVIzD4eSrT3eQEH+pXOcnxF/iq093SIVKPjqdjvHjxwN5PSGXL19ebXNZt24dAP369XO3RHA4HJw/f56zZ8+Sk5NT6hh//vmn+3GvXr2KPS7/c/nPEUIUT5MrOldim5SU5NF5ruOllEzUB3a7gwyLlXWrD/LjuiPYc51kZ+Xy/df72LrxGNbsXPcnB0LUdZ4EKnmly8YCX6U1UCwQpMiucQBs2LCBmTNnkpubi16v59lnn3V/EimEqJ1+2xJf7iDFJSH+Ett+jNdoRnXDLbfc4l72Mm/evGq5V7l06RKJiYkAtGvXjoyMDKZPn06fPn0YMGAAN910E9dccw1jx44tsZIkPj7v362/v7/7g+yi5N/Fx3WOEKJkmoQpzZs3R1VVVq9eXeabQafTyerVq93nC1GX2Wx2zp1OZ+mnOzl+uHDjsX27zrJ84S5SLmaSmyvLfkT94GmFSllJkFKQqqp88803zJ49G6fTidls5pVXXuGGG26o7qkJISogLTWLXzYc1WSsn9cfJS01S5OxqlpqaipHjx4t9ctVkVcW+atTkpKSWLZsWWVNv1j5l2Cqqspdd93FokWLsFgs7u/n5uaybds2xowZw7x584oc58KFCwCl7p7q7++Pj49PgXOEECXT5CrzxhtvJD4+nqNHj/Lqq68yderUEvdldzgcTJs2jSNHjqAoSonNlISozVSnis3mYOe2U+z67VSJa5mTL2Sw7LOdXD+wLR27NMNklptAUfflD1SOz52HmptbofEkSCnsp59+YtGiRQD4+fnxf//3f3To0KGaZyWEqKjdvyeUuUdKaZxOld2/JzBwaEdNxqtKS5cuZenSpZqPO3jwYNq3b8+RI0eYN28eo0aNqtLdRy9fvux+PH/+fHJycoiJieHpp5+mQ4cOZGRksH79ev79739z5coV/v3vf9O6detC91WZmZkAeHt7l/qa3t7eZGVleRQ8CVGfaVKZ8sgjjxAYGAjAihUruP3221myZAknTpxwNwl0OBycOHGCJUuWcMcdd7jXHwYGBjJ27FgtpiFEjWLPdZCelsXqpXvYsfVkmS54cm0ONq89zPpvY8m4koPDLst+RN2nVYWKBClFu+666+jSpQtBQUHMmDFDghQh6gBVVdm3U9ueg/t2ninTTmn1haIoTJgwAYCLFy9WSmBTkvyBRk5ODtdeey0ff/wxXbp0wWQy0ahRI+677z4++ugj925B//nPfwr9O3T1VSnLFs8mkwkAq9Wq1dsQok7T5GozMDCQOXPmMG7cODIyMjh16hSvv/66+3m9Xl9o5wVVVWnQoAFz5swhICBAi2kIUWPYbHZOHk1h89rDWLM9/6T92KGLXDhvYfCd0TRt3rDad/8QorJVtEJFgpTiGY1GXn75ZTIzMz1uFC+EqJks6dZitz8ur8wMG5Z0Kw0DS69gqEnGjx/vDj1KMnr0aI+3gh84cCBRUVHExcWxYMEC7r33Xry8vMo7VY9cXQXz/PPPF9l8vWfPntx8882sX7+e+Ph4jhw5UiA0N5vNZGdnk1uGv6uu3jBV9R6FqO0021Kge/furFixgj59+qCqaoEvu91e6Hv9+vVj5cqVdOvWTaspCFFjqE6VH9cdKVeQ4mJJt/LrpuOoyKdEon4ob4WKBCkFWSwWli1bVqCHmY+PjwQpQtQhF5MspR9Ug8atrRRFcfdOSU5OZsmSJVX22r6+vu7HjRo1IioqqthjY2Ji3I8PHDhQ5DjZ2dmlvqbrGFfvFCFEyTS96gwLC+Pzzz/n8OHDbNy4kQMHDnDx4kWysrLw8fGhSZMmdOnShYEDB0qZsajTFEUhvE1j4vadr9A4EW0boytl1xIh6hJPK1QkSCkoOTmZqVOncubMGSwWC48++mipOx8JIWqfXFvlNKu358ry4qvddNNNdOrUiYMHD7qrU6oibGjWrJn7cWnNY/M/n5aWVuC5kJAQUlJSSt111WKxuJcWhYSEeDpdIeqlSrny7NChg4Qlok6zWCwkJCQUWr7m0rRpU1q3a8y+Xacr9Dqt2wdx7nwily9fJiwsDH9//wqNJ0RtUNZARYKUgs6ePcurr75KSkoKAOnp6TidziLLwoUQtVtlLf81GDUrWq9TJkyYwOOPP05qaipffvkl48aNq/TXDAsLw2g0kpubW+z1pkv+SsSrf+dHRkYSGxuLxWL5/+zdeViUVfvA8e9s7IKACiK4pLnvW6ZppZapmUtqi6VmYlraYplLKu6UltnqWq+aZlZSZqXhnqai4r4rigImsuM4LLP9/uDHBLLDDCDcn+vieh/mOec898NrMHM/55yb2NjYPMsjX7t2LVsfIUTB5DemEMUQGRlJXFwciYmJuX79+++/+NSuip2dCrPJXKyv6l5VcHF1sFwrMjKyrG9biFJT0JIfSaRkd/HiRSZPnmxJpPTt25d3331XEilCVFA1vG3zcMVW497vHnvsMVq1agXAN998Y6mQY0sajcZyzaioqHw3B75x47+Hd/fOKmnbtq3l+OjRo3mOceTIkVz7CCHyJskUIYrB19cXT09PqlatmutXxrR6M/Ub1UChVBTrq0HjGqSlpuDk5ISnpye+vr5lfdtClKq8EiqSSMnu2LFjTJ8+nTt37gAwbNgwxowZY6nuIISoeFyrOuDsYmfVMZ1d7HCtKhuP5iVzk9uEhAS+++67UrnmE088AYBWq+XgwYN5tgsODrYc35sI6d69u+XvwS+//JJrf7PZzObNmwGoUqUKDz30UIniFqKykHehBYiIiODnn39m7969/Pvvv6SkpODp6Ymfnx8PP/ww/fr1y/NDrl6vZ+PGjfz+++9cu3aN1NRUvL296dq1KyNGjMDPz6+U70ZYi6urKy1atMi3jclkplGzmly5EFusazRp4YO7RxXcPWSTZlF53bvkx66qmyRSsti7dy9LlizBaDSiVCoZO3YsTz31VFmHJYSwMYVCQasOfhzYHWa1MVt18JM9lvLRtWtX2rRpw/Hjx/nf//5XKmWkBw8ezLJly0hISCAwMJANGzbg4uKSrc3mzZstVYq6deuGj49PtvNeXl706dOH33//nT179rBz50569OiRrc3atWu5dOkSAC+99JKlRLIQIn/yTjQfq1at4vPPP7fUZ8908+ZNbt68SUhICI6OjowcOTJH39jYWPz9/Tl37ly218PDwwkPD2fTpk0sWrSInj172vIWRBlSKhXUfsADOzsV6UXcKM6jmvN9V5pQCFvJTKg8+MZY7L29JJHy/27cuMHixYsxm82o1Wree+89OnfuXNZhCSFKSbuH63Bo71VMppJ/qFcqFbR7uI4VoqrY3nzzTV555RUSExMLbBsTE8O+fftyvAZw9+5dgoKCsp3r2rVrjqprLi4uTJs2jUmTJnHp0iUGDx6Mv78/jRo1QqvVsn37djZs2ABkVO2ZNm1arrFMnDiR/fv3k5iYyFtvvcXo0aPp1q0bBoOBbdu2WaoU1a5dm1dffbVwPwwhhCRT8vLpp5+ybNkyAJo1a8azzz5Lo0aNcHJyIi4ujtOnT/PXX3/lmsE3GAy88cYblkRK3759efbZZ3F2dubo0aMsXboUrVbLxIkT+f7772nevHmp3psoPSqVkjr1Pbl8/naR+j3QsJo8HRIiC5W9PVWaNkGhVEoi5f/Vrl2bF154gV9++YUPPviAli1blnVIQohS5O7pRLcnG7Jn28USj/Vor4a4e0o53IJ07tyZ9u3b57v3SKarV68yderUXM8lJibmOLd27dpcS9g/88wzxMfHs2jRIq5du5ZrwsTd3Z0vv/ySevXq5Xq9WrVq8fXXXzN+/Hji4+NZunQpS5cuzdbGz8+P5cuXU6VKlQLvTQiRQd6R5uLvv/+2JFLGjh3L22+/neODbdeuXXn99ddJT0/P0T8oKIgTJ04AMHLkyGy/LFu3bk3Hjh158cUXSUtLY8GCBaVas16ULrVGRf1GNYqcTGnY1MtmO/ULcb9SybTjHJ577jl69OiR6xtwIUTF16V7fa5djuF6WHyxx6hT34POj0v1lsKaMGECI0aMKNVrjhw5kk6dOrF+/XoOHjzI7du30Wg01KlTh8cff5zhw4fj5uaW7xjt2rVjy5YtrF27lp07d3Lz5k2USiW1a9emV69evPzyyzg7O5fSHQlRMSjMpbHg7z5iMpl48skniYiI4LHHHmP58uVFHuOpp57i2rVruLu7s3fvXuzt7XO0mT17tiWJ8tNPP5X4iaJOp6NNmzYAHD9+HCcnebpQXujuprPqs30Y9KaCGwNV3R156bVO2NlLrlMI8Z/09HS+++47Bg8eXOCbZiFE5ZGWauCHbw8XK6FSp74Hz4/qiL2DvOcQQoiikt+c99i3bx8RERFAxqyUogoLC7PUae/du3euiRSAgQMHWpIp27dvl+nZFZhao6TtQ7WJiylcGT2/uu4gK3yEEFnodDrmz5/P6dOnOXv2LPPmzZOkuRACAHsHNS+91ol/doXxd/ClQu2holQqeLRXQzo/Xh+VSip/CSFEcUgy5R7btm0DMtYeZs70AIiLi0Or1eLp6ZljF+2sjh07Zjnu0KFDnu2aNm2Kk5MTOp0uWx9R8ajVSjo+kvsa1rzY2cl/mkKIDAkJCcyePZurV68CYGdnh8lUuJluQojKQaVS0u2JB2nRthahB69z8kgEd7U5l6I7u9jRqoMf7R6uI3ukCCFECckntnucOnUKgIYNG2I2m1m3bh2rV68mMjLS0qZx48a89NJLPPvss5a67Zky3+wCPPDAA3leR61WU7t2bS5cuEBYWMFl7XQ6Xb7nU1JSChxDlA2lUomdvTz1EUIU3a1btwgICODff/8FoGPHjkyaNCnPWY9CiMrN3dOJnk83oUffxiQnpnL7VjIGvQm1RkkNb1dcqzrIBvdCCGElkkzJwmQyWZIhVatWZcKECWzfvj1HuwsXLjB9+nR2797NkiVLstViv3XrluXYy8sr3+t5e3tz4cIFEhISSE9Pz7eme9ZZMkIIISq+a9euMWvWLBISEgDo2bMnb7zxBiqVbE4thMifQqHAzd0RN3fHsg5FCCEqLHlcnsWdO3csU6f37NnD9u3bqVu3LkuXLiU0NJRjx46xfPlyy4yTnTt38sknn2Qb4+7d//bFKGg9u6Pjf3/gsvYTQghRuZ09e5Zp06ZZEinPPvssEyZMkESKEEIIIUQ5UaSZKTdv3rRVHPj4+Nhs7MLKulQmLS2N6tWrs2HDBjw8PCyvP/bYY7Rs2ZL+/ftz+/Zt1q9fzyuvvIK3t7elXyaNRpPv9bLORMnaLzfHjx8vMPbOnTvn20YIIUT5l5aWxkcffWRJso8aNYoBAwaUcVRCCCGEECKrIiVTunfvbpN1lgqFgnPnzll93KK6d5nN6NGjsyVSMnl4eDB27FjmzJmDXq8nODiY4cOHA2Rbx67X6/Nd156e/t/GYAWtf5eqDUIIUTnY29vz/vvvM3fuXMaOHcvjjz9e1iEJIYQQQoh7FHnPFLO54HJr96t7q/R06dIlz7aPPPKI5fj06dOWY2dnZ8uxTqfLN0mSdSZM1n5CCCEqt+bNm7Ny5UpcXV3LOhQhhBBCCJGLIiVTBg4caKs4ygU7Ozs8PDyIj48HoGbNmnm2zXousz1gWe4DEB0djbu7e55jZG5W6+7unu/ms0IIISouk8nEd999R/v27WnWrJnldUmkCCGEEEKUX0VKpgQGBtoqjnKjQYMGHD58GACj0Zhnu6zn1Or/foxZyyFfvXqVxo0b59n/xo0bANSvX79EMQshhLg/GQwGPv/8c/bs2cO2bdsIDAykbt26ZR2WEEIIIYQogFTzuUf79u0txxEREXm2y0yEQPYSyG3btrUcHz16NM/+Z8+eRafT5egjhBDCelLTDaSlG9Cl6tHq0tGl6klLN5Cabijr0EhNTWX+/Pns2bMHgKpVq8r+WEIIIYQQ94ki75lS0T355JN8/fXXAGzfvp3mzZvn2m779u2W46zJkPr161OvXj2uXbvGn3/+yeTJk3PdN+WXX36xHD/xxBPWCl8IIQQZSZS4pFR2h0Zw+UYCUTFa9AYTGpWSmtWdaeBblcfb16aGuyMOdqX/p/DOnTvMnTuXCxcuAPDggw8yc+ZM3NzcSj0WIYQQQghRdDIz5R5NmjSxbC67du1aLl++nKNNWFgY33zzDZCx30mvXr2ynR81ahQACQkJLF68OEf/06dP89NPPwHQrl07WrZsadV7EEKIyspgNJGkTeP7bRf4YOl+todcJ/zfZPQGEwB6o4kbt+6w62gEM5f9w5rfz5JwJxXD/58vDbGxsUyZMsWSSGndujXz5s2TRIoQQgghxH1EYa7I5XmKKSwsjKFDh6LVanF1dWX06NE89NBDABw5coSVK1eSlJQEwCeffMLTTz+drb/BYGDYsGGcOHECgKeffppnn30WZ2dnjh49ytdff41Wq8Xe3p7vv/8+z9kvRaHT6WjTpg0Ax48fl6niQohKx2A0EZOQwpIfjnEr7m6h+1Vzc+TtF9pS09MZtdq2zxgiIyOZOXMmsbGxAHTt2pW3334bjUZj0+sKIYQQQgjrKlIyZerUqbYJQqFgwYIFNhm7uEJCQnjrrbdISEjI9bxarWbq1Km89NJLuZ6PjY3F39+fc+fO5XreycmJRYsW0bNnT6vEK8kUIURll6RNY/7/QoiO1xW5r6ebA9Nf6YSHm4MNIvtPQEAAx48fB6BPnz74+/ujUqlsek0hhBBCCGF9RUqmNG7cGIVCYZNAzp8/b5NxSyIuLo61a9eya9cuoqKiMBqNeHt706lTJ0aMGJGtck9u0tPT2bhxI3/88QfXrl0jNTUVb29vunbtyogRI/Dz87NarJJMEUJUZqnpBtZtPc++E1HFHqNjM29efaa5TfdQSUxMZMqUKTz22GM899xzNvubKoQQQgghbKvIyRSbBKFQlMtkyv1EkilCiMosIvoOM5b9Q0nXrc4Y1Yn6vm42TXKkpKTg6Ohos/GFEEIIIYTtFenx286dO20VhxBCCFEsqekGdodGlDiRArA7NALfGi442Ftndsoff/xBSkoKgwcPtrwmiRQhhBBCiPtfkd4t1qpVy1ZxCCGEEMV26Xq8dca5EW+VpIzZbGbDhg388MMPALi5ufHEE09YYWQhhBBCCFEe2G5huBBCCFEKDEYzN2MLX70nPzEJKaTrjTiWYGaK0Whk+fLlbNu2DchIpBS0x5YQQojCCwkJYfjw4TleV6lUVKlSBRcXF3x8fGjevDkdOnSgW7duqNXysUcIYV3yW0UIIYTVJScnc/36dYxGo02v4+Ligkd1H1LT9FYbU5eaTnzMTZKSkorc12AwsGnTJs6ePQtA1apVGTFiBHfu3OHEiRP59lWpVNSpUwdXV9dixS2EEJWd0WgkMTGRxMREIiMjOXz4MN9++y1eXl6MHj2al19+WTb+FkJYjc2SKTdv3iQsLIzk5GT0ej0DBgyw1aWEEEIUQG/UYzSbUKDAjBkFCuxUGpu9qYyMjCQuLs4mY2dlNpup7qUAzJhM1ligAxqVkpg7d0hMTCxSv7S0NH777TciIiIAqFatGoMGDUKlUhV6LJVKRdOmTYscsxBCVFYvvPACL774ouV7nU5HUlIS586d459//uHIkSNER0czf/589u7dy5dffil7VwkhrMLqyZSNGzfyv//9j+vXr2d7/d5kytKlSzly5AheXl4EBgZaOwwhhKj0DCYjBpOBW3duczr6AjeTo9HpU1ApVXi7VKe+Rx2a1HgQtUKFndrOqtf29fXFaDTafGaKWq1GrVbiU70KN2O0JR7P080BR3s1arWaqlWrFrqfVqtlw4YN/PvvvwDUrl2bYcOGFekNu0qlwtfXt8gxCyFEZebp6UnDhg1zvP7oo48ybtw4Tp06xaRJkwgPD2f//v1MnjyZzz77TGaoCCFKzGrJlLt37zJ+/HgOHToEZDwtzJTbL6vWrVtbfpGNGjWKBx980FqhCCFEpZdmSONG0k2Czm7lasKNHOfP3r7Ezqv/UNXBlScadOWR2h2wU9mhVCqtcn1XV1datGhhlbEKkppuoHFdT2ISU0s8VuN61VCpiz475IcffrAkUjp27MikSZOwt7cvcTxCCCFKpmXLlvz4448MHDiQqKgo/vrrL4KDg+nVq1dZhyaEuM9Z510z8O6773Lw4EHMZjO+vr689tprPP/883m279SpE9WqVQNg9+7d1gpDCCEqvVRDGn9d+ZvF/6zMNZGSVWJqMj+d+YOvDq8lKe0OJrOplKK0HnuNisfa+lllrEfb+mKnURW539ChQ+natSs9evRg6tSpkkgRQohyxM3Njblz51q+X7FiRbbzISEhNGrUiEaNGhESEoLJZOKnn37ipZde4uGHH6Zx48bMnz/f0j6z7RdffJHvdbt3706jRo2YMmVKnm20Wi2fffYZffv2pVWrVnTq1Inhw4ezdetWAIKCgizXi4yMLM7tCyFsxCozU/bu3cuePXtQKBQMGDCAuXPnolar2bFjh6Us5L0UCgVdunRh8+bNHDt2zBphCCFEpZdmSOPPS7v46/LeIvW7FHuVr0LW8NbDo6hi72Kj6GxDoVDgW8OFto1qcOzi7WKP0+wBTxrUqoqykFO/zWazZealUqnknXfeQaVSydRxIYQoh7p06UL9+vUJCwvjzJkzREdH4+XllaNdWloar7zyimW2vS1FRkYyfPhwoqKiLK+lpqYSEhJCSEgI+/bto3379jaPQwhRPFZJpvz6668A1K1bl3nz5qFSFe6pXuPGjdm8eTNhYWHWCEMIISo1vdHA5bhrBF/+u1j9I5Ju8su5bQxt0Q8H9f01s8LeTsWwp5oQFpVEkjatyP1dHDW83KdpoWelhISEsH37diZPnoxGowEolbKbZqMBs9EACgWYzZb/VajUKFRSoE8IIfLz8MMPWz53HD16lL59++Zos2jRIi5dusSTTz5J//79qVmzJrdv37b6HmDp6emMGTPGkkh58sknGTRoENWrVyciIoJ169axadMmLl++bNXrCiGsxyrvvE6cOGGZlVLYRApgWeYTGxtrjTCEEKJSM5gMbDz9O2aKX9XmwI1QOtduzwMetVEqrLYS1OYUCgVuLna8/XxblmwIJelueqH7OjtqePO5tni6OqBUFjyrZMeOHXz55ZeYTCY+//xz3n333ZKEXigmfRpmQzq6y6GkRl1CH/8vZn0aCjt77Dx8sPdthFP9tijUdig11t1MWAghKoomTZpYjsPDw3Ntc+nSJSZMmMD48eMtrzVr1szqsaxbt86S2BkzZky2vyXNmzenV69evPXWWwQHB1v92kII67BKMiWz/GXt2rWL1C/zaZ5er7dGGEIIUWmZTCYuxFzh9t2SJafNmNlz7SB+bjWxv89mp2jUKvy8XPhgVCfWbT3HqSsF/yya1vNgeJ9meLo5FGpWyqZNm1izZg0AdnZ2dOvWrcRx58dsMmE2pHHn5G6SQ//ClHY3Rxt9TAR3L4aQ+E8Qbu1749LiURQaOxT3UTJMCCFKQ9YqbcnJybm2eeCBB3j99ddtHsvGjRsBqFWrFm+++WaO80qlktmzZ/P333+TmlryDdaFENZnlWSKvb09er0eg8FQpH7x8fFAxqZQQgghii/dpOfErXNWGetM9EVM5uLPbilLGrWKGu6OvDG4NWFRiewJjeRyRAIJd/5b+uPmbEcDP3cea+tLw9ru2GlUBc5IMZlMrFmzhl9++QUAZ2dnZsyYUeSqP0VhNpsw6pKJ3bqctJsFT/M2pWpJ2P8TKTfOUq3XqygdqqCwUnUmIYSoCJycnCzHd+/mTE4D9O7d22qV7fJy69Yty8yY3r17Wx4w38vDw4NHHnmEHTt22DQeIUTxWCWZUqNGDbRabZH3Pjlx4gQAfn7WqcIghBCVlQKITPrXKmOlGFJJSEnCUeNglfFKm0KhwMFeTZN6ntT3rYrZDLpUPWnpRuw0KpwdNSgUYKcuOIkCYDAY+OKLLyyV5zw8PJg9ezZ16tSx6X2Y0lKI+eNr0m9dLVK/1BvniPljGTWemYDC3qngDkIIUUnodDrLsYtL7putN2rUyOZxZN0HpaAlRM2aNZNkihDllFWSKR06NRlrVQAAIABJREFUdCAsLIytW7fy5ptvFiqbGxsbS3BwMAqFgoceesgaYQghhM0lJydz/fp1q29EVxJ2dnbUqV+XOG2C1ZZNxt1NwHzHQExMjFXGK0v29vbY29ujVCpJNZmINxjw9vbGwdW1wL5paWl89NFHHD16FMiYjj1r1qxcK0BYk0mfRtLhP4qcSMmUdvMyyceCcW3fG6Xm/lquJYQQtpKQkGA5ds3jb0Ber1tT1iVG7u7u+bb18PCwdThCiGKySjJlwIAB/PDDD9y4cYNPP/20wM34UlNTeffdd0lNTUWtVjN48GBrhCGEEDYXGRlp2SeqvHBwcMD8/8tyTGaTVcZUkPEELzEx0SrjlTcGg6FQS3QuXLjAsWPHAGjQoAEBAQGlsjTVeCeeOyd3lmiMpNBtODftgtKtupWiEkKI+9v58+ctx/Xq1cu1ja2X+AghKg6rJFNat25N79692bp1K6tWreLGjRuMGjUqxx4q0dHR7N+/n5UrV3L9+nUUCgXPP/+8LPMRQtw3fH19MRqN5WpmikKhQKFQ4OVSjeS0O1YZ08ulOtrU5Gyb9VUUKpUKX1/fQrVt1aoVr7/+Ovv27WPq1KnZ1tvbikmfxp0zf4OphP/GjAa0Z/7GrePTMjtFCCGAAwcOWI7btWtX7HEUCgVmsxmTKf8HGFmXFWWVdfZL1tkyucncY1IIUf5YJZkCsGDBAm7evMnJkycJDg62lPFSKDLWozdt2tTy5BTAbDbTuXNnpkyZYq0QhBDC5lxdXWnRokVZh5FDqiGNetVqE54cWeKxPB3dqeLgTPV6nlaI7P5jMpmyPZl88skn6dmzZ7bX9EYDxjySHSqlCo2qBH9ezWZSb5wtfv8sUiPO49bxaauMJYQQ97P9+/dz7do1ICNRXqNGjWKP5ezsjFar5c6dvB9gJCYm5jm7s0GDBpbjs2fP0qdPnzzHOXvWOn8PhBDWZ7V5bI6Ojnz33XcMHz4ctVqN2Wy2fEHGm9PM79VqNa+88gorVqxArbZaPkcIISotB7U9D/u1tcpY7Wq1qLRldS9fvsw777zDrVu3sr1+77Rvo8nIpwdWMmvX4mxfnx5YmWeSpbDMRj36hOgSjZFJH3cTytEsKiGEKAtJSUnMnDnT8v2YMWNKNF6tWrUAOHPmTJ5ttm7dmu1BclY1a9a0bGK+devWPPc7i4+PZ//+/SWKVQhhO1bNZNjZ2TFt2jT8/f3ZunUrR48eJSoqCq1Wi5OTE15eXnTo0IG+ffvi7e1tzUsLIUSl5+1SnabVH+RcTMFldPPioLanW92HsFfbWTGy+8OJEycIDAwkJSWFWbNm8dlnn2Fvn/fymKTUOySkJpX4ulk3NXZ1dcXH3Rl9WmqJxwVAr8eQnkJE1L/odDrq1KlTKpsrCiFEeXHq1CkmTZpEVFQUkFGKuGfPniUas2PHjly8eJETJ05w4sQJWrdune18eHg4S5YsyXeMIUOG8PHHHxMVFcUXX3zBxIkTs503mUzMmjWL1FQr/T0QQlidTaaFVK9eneHDhzN8+HBbDC+EECIXGqWG51s+w4K9X5JqSCvWGP2bPImbQxUrR1b+7d+/n8WLF2MwGFAoFDzzzDP5JlKsKeumxkqlEjyqYDLl/jSzWBQq7ty5Q2JiIiqVqlAb7wohxP0iLi6OS5cuWb5PSUkhMTGR8+fP888//3D48GHLuW7duvHhhx+W+JpDhgzh+++/x2g08tprrzFhwgRat25NamoqISEhrFmzhqpVq6JUKvPc82T48OFs2rSJa9eusXz5cq5du8bgwYOpVq0aERERrFu3jiNHjtCiRQtOnz4N/Ld9ghCifJA1NkIIUUEolUrcHasyqu1zrDz6PXqToeBOWXSt05HOtdtjp6pcs1L+/PNPli9fblmGOnHiRB555JFSu37WTY01Gg0qe0c0Lm4YdckFdy6A2rUaCrUGOzs7PD09C73xrhBC3C82bNjAhg0b8m3j7e2Nv78/w4YNs0pColGjRrzzzjt8/PHHJCYmMnfu3Gzna9asydKlS/H3989zDHt7e1auXMmIESOIiorKtudkpmeeeYaOHTtakimlleQXQhSOJFOEEKICsVNpaFKjAeM7jeS7E0HE6gquAqBRqunTsDvd63fBQV153qiZzWY2bNjADz/8AGTs/TVt2jRatWpVqnHcu6mxSZ+Gc60GpISfLvHYTrXqy2wUIUSloVKpcHZ2pkqVKtSqVYvmzZvTsWNHunXrhkqlsuq1/P39efDBB1mzZg1nzpwhLS2NmjVr0qNHD0aPHo2Hh0eBY/j5+fHbb7+xcuVKgoODiYqKwtHRkQcffJDBgwczYMAAVq9ebWlfpUrlmzkqRHmmMOe1M1IRaLVaFixYgNlsZtCgQXTo0KHAPkeOHCEoKAiVSsX06dNxcHAoaRiVmk6no02bNgAcP368VMp3CiHKL4PRQIohlV3XDnDoxjHiU3JWFLBTaWhTsxlPNOhGDedqlWqfFKPRyMqVK/nzzz8BcHNzIyAgIFuFhfyk6tOYtWtxjj1T3B3cmNV9Ig4lKEVsNhnRXTlG7NblxR4jU/V+E3Cs2wKFsnJuKCyEEPe7Dz74gJ9//hkvLy/+/vvvsg5HCJGFVWam/PnnnwQFBeHg4MC0adMK1adx48Zs27aN1NRU2rdvz4ABA6wRihBCCECtUlNF5ULvBo/xZP1uxOjiCU+IQJt+F7VSTW03H2q5eqNRabBX2VW6ddhJSUkcOnQIgBo1ajB79mxLdYZM+ZU/1pv0pBtzVl9IN+rRm/SQe2GGQpVNVihVONZpjqaaL/rY4pe6tvOqh4NvI0mkCCHEfSo1NZWdO3cC5NjkVghR9qySTNm3bx8AjzzySKGnn1WpUoWuXbsSHBzMnj17JJkihBA2YPf/s01qu/ng51oTo9mIEmWOUr+VjYeHB7NmzWLZsmVMmjQJT0/PHG0yyx8npd7JcS7dqOeuXpfj9bt6HQE7F2On0uQ45+ZQhXc6+xeYTAFQqO3w7D6cW5sWgrFoe9/81/9lFOqccQghhCgfIiMjqVmzZq5LkIxGI7NmzSIhIQGA/v37l3Z4QogCWCWZcv78eRQKhWWZSWG1adOG4OBgzp8/b40whBBC5EOhUKBWVN6tsgwGA2r1f/dft25dAgMD852Vk5CaRKw2HoqwIDZRnwSKjLX7SkXxklYKlQpNtVpUe+IVYoO/hTxmyOTeV4Pnk6+idvdCobTuHgFCCCGs56effmLLli3069ePtm3bUr16ddLS0rh06RIbN27k7NmzADz00EN07969jKMVQtzLKu+qY2JigIydq4vCy8sLgNu3b1sjDCGEECJX0dHRzJ49m5dffpmHH37Y8npBy5uMRhMmk6noFzQDKFCqiz8DSKmxx/GB1tTo/xbxu77DkBRTYB91VS88ewzHzqsuyhLs2yKEEKJ0REVFsWzZsjzPt2rViiVLllS65bhC3A+s+oiyqHvZZr5BNRiKPoVZCCGEKIzw8HBmzZpFfHw8H3/8MZ9++im1a9cuVF+V6v+XRBV1q3ZFRt+SUmrscajVEO/npqE9u5+75w+gj/83RzuNZy1cmnbBuUlnlHYOKAqxlEgIIUTZeu6553Bzc2P//v1EREQQFxdHeno6VatWpVmzZvTu3Zt+/fpZvRKREMI6rPJuy93dnejoaK5fv16kfjdu3AAyqigIIYQQ1nbu3Dnmzp3L3bt3AXj66afx9fUtdH8lSjRlvO+IQqVG5ViFKq17UKXl45jSdKTHRmJOT0Vp74jG0xelvSMolSgrUUUmIYS43/n4+DBq1ChGjRpV1qEIIYrBKsmUxo0bc+vWLYKDg3n99dcL3e+vv/5CoVDQsGFDa4QhhBBCWBw+fJiFCxeSnp4OwMiRIxk0aFCRxnBzyH1T9bw2oAVw1jjluQFtSWQmSpR2DqireJRoLCGEEEIIUTJWSaZ069aNPXv2cPHiRdatW8dLL71UYJ/vvvuOixcvolAoePTRR60RhqiEDAYTJmPu+xkoVUrUJdivQAhx/9q5cydffPEFJpMJpVLJhAkT6NGjR5HGUClVvNPZP9dzepOegJ2LcyRUnDVOzO7xLhpl7n9eVbIhrBBCCCFEhWCVZMqgQYP4+uuviYuLIzAwkNjYWMaMGYOTk1OOtjqdjuXLl7Nq1SoUCgXu7u4MGTLEGmGISshkNPHz2lC02rRsr7u42DN4eDuQZIoQlU5QUBCrV68GwM7Ojvfff5+OHTsWeRyNSp13GWM92Kk03NVnf9lOpUGjVOMgm78KIYQQQlRoVkmmODg4sGDBAsaNG4fJZGL58uWsW7eOhx56iPr16+Pk5IROpyMsLIyQkBDu3r2L2WxGpVIRGBiYa9JFiMLSatPQJqcV3FCICsJkMqFAgUIpO/vfy2g0cuLECQCcnZ2ZMWMGTZs2LeOohBBCCCFERWO17f67devGokWL+OCDD0hJSUGr1bJr1y527dqVrV1mxR8nJyfmz58vS3yEEKIIzCYzKTo9SqUCBweNJFTuoVKpmDJlCosXL2bYsGHUq1evrEMSQgghhBAVkFVrJ/bp04cWLVqwYsUKtm7dilarzdHGxcWFvn374u/vX6SKCkIIUdmZTWZ0unR+++EkKrWSfkNbSkIFSE9PR61WZ5QwJiNZP3369DKOSgghhBBCVGRWTaYA+Pn5MXfuXGbPns3Fixe5desWWq0WFxcXvL29adSokeUNrxBCiMLJmkj5NyoJgC0/nqr0CRWtVsu8efOoV68eY8aMQaGonD8HIYQQQghRuqyeTMmkVCpp0qQJTZo0sdUlhBCiUsgtkQIQeT2hUidU4uLimDVrFtevX+fcuXM8+OCDdO/evdSun1up45KWPxZCCCGEEPcHmyVThLCW/MofGwwmDPqc5wx6EwaDCTDkOCclk8X9JK9ESqbKmlCJiooiICCA27dvA9C5c2e6du1aatfPr2yylD8WQgghhKj4FObMHWHFfU2n09GmTRsAjh8/XqEqJKWnGXItfwwZSZPUFH0uvcDBUYNakz1pklky2c5e8oii/CsokZKVbx33SpNQuXLlCrNnzyYpKeNn0rt3b8aMGYNKJUkMIYQQQghROuQTpSi25ORkrl+/jtFotNk1NBoND9RrQGLiXe4kpRapr16fM8liMBgwGo2cPXsx1/MFUalU1KlTB1dX1yL3FaIoipJIgcozQ+XkyZMsWLCAlJQUAJ5//nleeOEF2StFCCGEEEKUKkmmiGKLjIwkLi7Optewt7fHbDZjNmV8lZTZZMZsNpOcnExaWs6ZLoWhUqlo2rRpiWMRIi9FTaRkqugJlX/++YdPPvkEg8GAQqHgtddeo0+fPmUdlhBCCCGEqIQkmSKKzdfXF6PRaPOZKQqFAoVSYZUPhgqlAoVCgaura7FnpkhJb2FLxU2kZKrICZWoqCgMBgNqtZqJEyfyyCOPlHVIQgghhBCikpJkiig2V1dXWrRoYfPrpKcZqFrVGbU65z/X4uyZolKpaNasmU1iFaIkSppIyVRREypDhgwhNTWVli1b0rp167IORwghhBBlJCQkhOHDhwOwdu1aHnrooTKOSFRGkkwR5Z5SpWTw8Ha5njMYTKz+6kCOhIqDo4aRb3TOtWqPUiWVfET5Y61ESqaKkFAxGo2kpKTg4uICgEKhsLxxEkIIUXll/SCdlUqlokqVKri4uODj40Pz5s3p0KED3bp1y/WhnBBClIR8qhTlnlqtxM5eneuXWq3MMfsEQK1R5tlPyiKL8sbaiZRMmQmV1FS9VfYcKk16vZ5PPvmEGTNmoNPpyjocIYQQ9wGj0UhiYiKRkZEcPnyYb7/9lnHjxtG9e3fWrl1LRS5iGhQURKNGjWjUqBGRkZFlHY4QlYKkaIUQogzZKpGS6X6coaLT6QgMDOTkyZMArF69mtdff72MoxJCCFEevfDCC7z44ouW73U6HUlJSZw7d45//vmHI0eOEB0dzfz589m7dy9ffvkljo6OZRixEKKikGSKEEKUkfwSKWaTGaPRiJmiP0VToEClUlkSJ/dTQiUpKYnZs2dz5coVABo3bszLL79cxlEJIcT9xWw2Ex0dzZUrV0hNTcXBwYEGDRrg5eVV4UrJe3p60rBhwxyvP/roo4wbN45Tp04xadIkwsPD2b9/P5MnT+azzz6rcD8HIUTpk2SKEEKUEaPRRGR4ArduJuc8ZzJiMpmKNa4ZMyhArfzvV/zNiERuRiRR5wEP1EpVsWO2pdu3bxMQEEBUVBQA7du3Z/Lkydjb25dxZEIIcX+Iiopi06ZN/P7778THx+c47+HhwdNPP82zzz5LrVq1yiDC0teyZUt+/PFHBg4cSFRUFH/99RfBwcH06tWrrEMTQtznbJ5MuX37Nj///DNHjx4lOjoagBo1atC+fXsGDx6Ml5eXrUMQQohySa1R8UDD6vTo05idf17ItpZbpVSBmeLPTMmSMFEqFfTq34za9TxQa8pnIuX69esEBARY3vw//vjjTJgwQTYMFEKIQjAYDKxevZqVK1diNBrzbJeQkMDatWtZv349/v7+jBw5slL8nnVzc2Pu3LmMGjUKgBUrVuSZTNm+fTu//PILp06dIjExEWdnZxo0aECvXr14/vnnsbOzK/B6ISEh/Prrr4SGhhITE4Ner6dGjRo0bdqUxx9/nD59+uS51Ojs2bNs3LiRkJAQbt++jdlsxsvLi06dOjFy5Ejq1auXrX1kZCQ9evTI9tq930POijcnTpxg165dHDt2jLCwMJKTk3FwcMDHx4dOnToxfPhw/Pz8co1xxowZ/PjjjwAsXbqU7t2759pu/fr1zJkzB4AJEyYwfvz4Et+vEOWJTX97/vHHH8yYMYOUlJRsHxLCwsI4dOgQq1atYs6cOfTr18+WYYgKzsUl51Pr3F4TojzS2Klo0rImQLaEikKpyDazpLgyEykNGtdAY1c+Eynnz59nzpw53L17F4D+/fvzyiuvoFTKZtFCCFGQu3fvMnHiREJDQwtsm/k3xmg0smzZMo4cOcLixYtxdna2dZhlrkuXLtSvX5+wsDDOnDlDdHR0toe6qampvP322+zevTtbv8TERI4ePcrRo0dZv349q1atyjPJoNPpmDJlCn/99VeOc1FRUURFRbF9+3YUCgWDBg3Kdt5kMvHRRx+xZs2aHBvlhoeHEx4ezs8//8zMmTN57rnnivtjADI2q506dWqO17VaLZcuXeLSpUts3LiRhQsX8tRTT+VoN3XqVEJCQrh+/TrTp09ny5YteHp6Zmtz9epVFi1aBEDr1q0ZN25cmd2vELZis2TK0aNHef/99zEajbi5udG1a1e8vb3R6XRcuXKFI0eOkJKSwpQpU/Dx8aFdu9xL3wqRn/zKJksJZHG/yCuhUlL3QyIFMt5Q6fUZ5c1HjBjBoEGDZC27EEIUgsFgKHQiJTehoaFMnDiRr776qlLMUHn44YcJCwsDMj6r9O3b13Ju0qRJlkRKixYtGDlyJHXr1iUuLo5Nmzbx119/ER4ezogRI/jtt99wcXHJNrbJZGLcuHEcOnQIgPr16/Piiy/StGlT7O3tuXXrFkePHuWPP/7INba5c+fy/fffA9CxY0cGDhyIn58f9vb2XLhwgTVr1nDlyhVmzpxJtWrVLLNPvLy82LJlCzt37mTJkiUAfPPNN9SoUSPb+L6+vpZjo9FI1apV6dGjB+3bt6dOnTo4ODgQHR3NiRMnWL9+PVqtlkmTJlG/fn0efPDBbGM5OTmxcOFChg0bRlxcHB988AHLli2znNfr9bz33nukpKRY2qpU2d+HFPd+hShPbPZbc/ny5RiNRgYOHEhAQAAODg7Zzl+4cAF/f39iYmJYvnw5K1assFUoogJTq5UgpY5FBWDthMr9kkgBaNasGe+//z5JSUk88cQTZR2OEELcN1avXl3sREqm0NBQ1qxZw6uvvmqlqMqvJk2aWI7Dw8Mtx7t37yY4OBjI2Lj266+/zpZcevTRR/n888/56quviIqK4quvvmLy5MnZxl67dq0lkdKnTx8WLlyIRqOxnG/WrBk9evRg4sSJJCdn3yvtn3/+sSQWPvzwQwYOHJjtfMuWLenfvz9jxozh0KFDzJ8/n0cffRS1Wo1Go6Fhw4acOXPG0r5u3brZkif36tatG/369cvx+axZs2Z0796dYcOGMXToUG7dusXy5cv5+OOPc4zRunVrxo4dy5dffsnu3bv58ccfGTp0KABffvklZ8+eBTJmsdSpU8dq9ytEeVLkT6FarbZQ7U6ePIm9vX2uiRTIqNAwZswYIGPNnhBCVHaZCZUefRqXaGbG/ZBIiYuLy/Z9x44dJZEihBBFEBUVxcqVK60y1ooVKyybf1dkVatWtRxnTWisX78eAHt7e+bNm5frh/Y33njDMkPj559/Jj093XLOaDTy7bffAlCrVi0CAwOzJVKy0mg0OZbEZD5U7tOnT47EQiZ7e3tmzpwJZPx/HxISkv/N5sPLyyvXz2dZz48ePRrISDTltSH+uHHjaNWqFQCBgYFcv36d0NBQy7/Lxx9/3JJgyaq071cIWylyMqV3795s3bq1wHZpaWk4ODjk+x9q5i+0tLS0ooYhhBAVUkkTKuU9kWI2m1m9ejVvvPEGV69eLetwhBDivrVp06Z8N5stCqPRSFBQkFXGKs+cnJwsx5n7dBkMBo4cOQJA165dcyyPyaRSqXj22WeBjERM5swLyNj7K7PQxtChQ/P9/HMvrVbL4cOHAQqsMFS/fn3c3d0BOH78eKGvUZC7d+8SERHB5cuXLXumZFbS02q1REZG5tpPrVazcOFCnJyc0Ol0vPfee5ZtHjw9PZk/f36OPuXhfoWwliLPlYqJiWHixIn8+uuvzJw5M8+yanXr1uXSpUvs2rUrzx2ef/31V4AcU7+EEKIyK+6Sn/KeSDEajXz55Zfs3LkTgM8//5xPP/1U9kcRQogiMpvN/P7771YbT6FQsGXLFsaPH1+hfyfrdDrLceaeJxEREaSmpgIZy0vyk/X85cuXadOmDZCRTMnUvn37IsV07tw5y8yPt956q9D9YmNji3Sde8XFxfHNN9+wfft2IiIi8n2vkZiYSO3atXM9V7duXaZMmcLMmTM5deqU5fV58+blmIEDZXe/QthCkWemfPTRR3h4eLB3716efvppVq1alWtWfODAgZjNZt555x3mzJnDwYMHuXr1KmfPnmXz5s0MHTqUAwcOoFAoGDBggFVuRgghKoqizlAp74mUtLQ0AgMDLYmUmjVrMmXKlAr9pl0IIWwlOjraUkreGsxmM/Hx8ZbZFRVVQkKC5djV1RWApKQky2seHh759q9evbrlODExMddxs7YpjHuXvRZWZgKoOE6fPk2fPn345ptvuHHjRoEPbQq61nPPPUfz5s0t3/fv3z/Ph+llcb9C2EqRZ6b079+fxx9/nIULF7Jp0yY++eQTtmzZwpw5cyxr5gBeeuklDh48yN69e9mwYQMbNmzIMZbZbKZLly4MHz68ZHchhBAVUGFnqJT3RIpWq2XevHmcO3cOyJi2GxAQkG3tuhBCiMK7cuWKzcb19va2ydjlQdYZJPXq1ctxviwS/Fn3I5k3b162z1P5cXNzK9b10tPTefvtt0lMTESj0fDyyy/To0cP6tati6urK3Z2dgAcPHiQkSNHAhSYbAkNDc32sz1x4gQ6nS7bsqpMpX2/QthSsbZEdnV1Zd68eQwYMICAgAAuXrzICy+8wNChQ3nvvfdwcXFBrVazdOlS1q5dy3fffZdjUysfHx+GDRvGyJEjc5TKEkIIkaGghEp5T6TEx8cza9YsS9WEFi1a8MEHH+T6BksIIUTh2OopfUXfx/DAgQOW43bt2gHZP6QXNGsiJibGcpz1gUDmvh6ZbYqyhUHWcZycnGjYsGGh+xbHoUOHLHugBAQEMGTIkFzb3VtxKC9ardayT4qLiwtarZbr168TGBjI3Llzc7Qv7fsVwpZKVFO2ffv2bN68mbfffhs7Ozs2btxI7969+fPPPzMGVyoZOXIkO3fuZM+ePWzcuJGNGzeye/dudu3axauvviqJFCGEKEBeS37KeyLl1q1bTJ482ZJI6dy5MwEBAZJIEUKIEirKBqdFkbnpaEW0f/9+rl27BkCrVq0sG836+flZfp5Z9/zIzenTpy3HmZV9AJo2bWo5Pnr0aJHiatKkieVv+7Fjx4rUN6vCzqrJOqupd+/eebbLWmo5P/Pnz7ckZz799FNLdZ4ff/yR3bt352hvrfsVojwoUTIFMnZxHjt2LFu2bKFz587ExMTw7rvv4u/vT0REhKWdt7c3rVq1olWrVtSsWbOklxVCiErl3oRKeU+kADg6OlrKSz711FNMmjTJMn1YCCFE8TVo0OC+GresJSUlWcrsAowZM8ZyrFar6dChAwD79u3LNvskK5PJxKZNm4CMWfrNmjWznGvSpIlledSPP/5YpJlDHh4etG7dGoDffvst2/4rRZE1EZa1bPO9DAaD5TivOFNTU9m8eXOB19yxY4elCtQLL7xAt27dmD59uqVAyfTp03Ps7WOt+xWiPChxMiWTn58f33zzDR9//DEeHh7s27ePZ555hhUrVlitbJsQQlRmloRK38blPpECGVOnZ8+ezciRIxk3bpzMRBRCCCvx8vLCw8PDqnt8eHh44OXlZbXxyotTp04xdOhQy5YDvXv3pmfPntnaDBs2DMhY5jRjxoxcP7t8/fXXXLp0CYDBgwdnezigVCoZNWoUAFFRUUybNi1b0iIrvV6fYznRuHHjgIylNW+++SZ37tzJ837S09NZv359jiVZWTe+zfpA+15ZlyD98ssvOc6bTCZmzpxZ4GbEMTExTJ8+Hcio6DN58mQgo0rSwoULUSqVxMbGMmPGjBx9rXG/QpQHqlmzZs2y5oANGzZk6NChJCUlcerUKQ4dOsT27duzZWyF9en1epYvXw7A2LFj0Wg0ZRx1jLj/AAAgAElEQVSREMIWVColHtWc8ajmXC4TKZGRkZYKCQDOzs7ZpvQKIYQoOYVCQUJCAidPnrTamEOHDuWhhx6y2ni2FBUVZUkE1K9fHx8fH+Li4oiLiyMyMpKLFy+ybds2PvvsM5YsWWKpvNOtWzcWL15smTWZqV69ely6dImwsDDCw8PZv38/zs7OpKenc/78eZYsWcL69esBqFWrFp9++mmOmZYtW7YkNDSUyMhILl++THBwMABGo5H4+HjOnj3Lpk2bmD59Ot7e3jRp0sTSt27duiQnJ3Py5EnLvel0OoxGI1qtlhs3bnDo0CHWrVvH9OnTCQ4Oxt/fP1sMbm5urFmzBqPRyNWrV/H19cVoNJKcnExSUhIODg6o1Wp8fHwss2cOHz5MXFwcKpWK5ORkDh48SEBAAHv37qVNmzbcunULyKjS6uvrm+1+33nnHS5cuIBarWb58uXZzvv4+JCWlkZoaChXr16lZs2a2ZZCWeN+s/4byC0+IUqDwlzQ9swlcPz4cWbOnMnly5dRKpUMGTKE9957jypVqtjqkpWWTqez1Lo/fvy47EkghCh127ZtY9myZbz22mv5rsMWQghRclFRUQwaNMgqM8BVKhVBQUGW5RnlXUhISJGqgXp7e+Pv78+wYcPyTO6npqby9ttv57rPR6a6deuyatUq/Pz8cj2v0+mYNGkSO3bsyDeewMBABg0alO01s9nMV199xdKlS/Oc1ZLJycmJgwcP5tg7Z9GiRaxatSrXPmvXrrUky/bu3cv48ePzXA7Uq1cvnn/+eV555ZUcfQE2bNhA5vP4CRMmMH78+Bxj6PV6hg4dyrlz53B2dmbz5s3Zfm4lvd+s/wbujU+I0lLsZEpSUhK//fYbJ06cIDExEUdHR+rXr0/v3r1p3LixpZ3BYODbb79l6dKlpKam4unpydSpU+nbt6/VbkJIMkUIUXbMZjMbN27k+++/BzLe9KxYsSLbDBUhhBDWt2rVKpYtW1biccaNG8err75qhYhKR17JFJVKhbOzM1WqVKFWrVo0b96cjh070q1bt0IvNd2+fTtBQUGcPn2axMREnJ2dadCgAU8++SQvvPBCofb+2r9/P7/88gvHjx8nNjYWs9lMjRo1aNasGT179uSpp57Kc5yIiAh++OEHDh48SFRUFHfu3MHBwYGaNWvSpEkTHnnkEXr27ImLi0uOvmazmZ9//plff/2VK1eucOfOHUuy7d6Ew8WLF1mxYgUhISEkJibi5uZGo0aNGDhwIP369cszWREeHs6AAQNISUmhZcuWbNiwIcdMn0xXrlxh0KBBpKWl0a5dO9atW4dSmX2XieLeryRTRHlQrGTKjh07mDp1KlqtFsj4DzdrlnfYsGGWNXSZoqKimDVrFvv27UOhUNClSxdmzZolU7KsRJIpQoiyYDKZWLlyJX/88QcAVapUISAgQEodCiFEKTAYDLzxxhuEhoYWe4x27drx1Vdf5fmBWAghRO6KnEw5c+YMzz//vGU6VvPmzfHz8yMxMZGTJ0+i0+lQKBS88cYbuU75+vPPP1mwYAGxsbE4ODjw+uuvS4lkK5BkihCitOn1epYsWcK+ffsAqFatGnPmzJEkuRBClKK7d+8yceLEYiVU2rVrx+LFi3F2drZBZEIIUbEVOZkyfvx4duzYgYuLCytWrKBt27aWc/Hx8bz55pscPXoUR0dHDh06lGu9eq1Wy8cff8yPP/6I2WymQYMGbNmypeR3U4lJMkUIUZpSUlL48MMPOX78OJBR0W327NlUq1atjCMTQojKx2AwsHr1alauXInRaEShUJDbW/zM11UqFWPGjGHEiBEyI0UIIYqpyKWRQ0NDUSgUjBo1KlsiBTJKqgUEBAAZmzidPXs21zFcXFyYNWsWGzZsoGHDhly5cqUYoQshhCgLOp2O6dOnWxIpjRo1IjAwUBIpQghRRtRqNaNHjyYoKIgRI0bg7u6eazt3d3dGjBhBUFAQr776qiRShBCiBIr8GzRznxQfH59cz9esWTNH27y0atWKoKAg1qxZU9QwhBBClBEHBwd8fHy4fPky7dq1Y/LkyTkqCgghhCh9tWrVslRXiY6O5sqVK6SlpWFvb0+DBg3w8vKSUvVCCGElRU6m1KxZk4iICA4cOMCAAQNynD948KDl2Nvbu8DxVCoVo0aNKmoYQgghyohSqeTNN9+kfv36PP300/JkUwghyhmFQoG3t3eh3osLIYQoniIv8+nZsydms5ktW7Ywb948rl69Snp6Ordv3+ann35i+vTpKBQKateuLdUchBCigrh27Rp6vd7yvUajYcCAAZJIEUIIIYQQlVKRN6BNTk5myJAhXL9+PddpgmazGbVazcqVK3n44YetFqjIn2xAK4SwldDQUAIDA2nfvj2TJk2S6mtCCCGEEKLSK/LMFFdXVzZs2ECfPn1QKpWYzeZsX40bN2b16tWSSBFCiApg9+7dzJs3j/T0dA4dOsTFixfLOiQhhBBCCCHKXLHmZ3t4eLB48WISEhI4c+YMiYmJODo60qBBA+rWrWvlEIUQQpSFzZs388033wAZy3omTZpE06ZNyzgqIYQQQgghyl6JFru7u7vTtWtXa8UihKjgDHojJpM5Y4mgAswmM2qNEqWyyJPkhA2ZzWbWrl3Lpk2bAHBycmL69Ok0b968jCMTQgghhBCifJCdA4UQNmUymTAYTCTE3uXyhRhio++gu5uOUqmkqocjXjVdebCpF/b2ajR2shdHWTMajXz11Vfs2LEDgKpVqzJ79mzq1atXxpEJIYQQQghRfkgyRQhhM/p0I3ExWvYGXyLqRmKO8zcjEjl38l/277pC89Y+dHr0Aezs1ahUMlOlLJjNZhYtWsSBAweAjPL2c+bMkdKaQgghhBBC3EM+sQghbCI93cCp0Eg2/u9IromUrPTpRo4fjmDDN0eIj7mLwWAqpShFVgqFgvbt2wNQr149PvroI0mkCCGEEEIIkQuZmVJI77zzDn/++afl+507d+Lr65tne71ez8aNG/n999+5du0aqampeHt707VrV0aMGIGfn19phC1EmdCnGzkdGsXe4EtF6pcYryNo/TGGDG+Pu6cTCmXO8uvCtnr27ImdnR3t2rXD2dm5rMMRQgghhBCiXJJkSiHs3bs3WyKlILGxsfj7+3Pu3Llsr4eHhxMeHs6mTZtYtGgRPXv2tHaoQpQ5k8lEXIyW/TsvF6v/XW06wVvOMWhYG+zs5VeUrd28eROlUpltBkq3bt3KMCIhhBBCCCHKP1nmU4CUlBRmz54NgKenZ4HtDQYDb7zxhiWR0rdvX7799ls2btzIpEmTcHFxQafTMXHiRM6cOWPT2IUoCwaDib3BlzAazcUe42ZEIhfPRmOU5T42FRYWxuTJk5k5cyYJCQllHY4QQgghhBD3DUmmFODzzz8nKiqKTp06FeppbVBQECdOnABg5MiRLF68mC5dutC6dWtGjx7N//73PzQaDWlpaSxYsMDW4QtR6uJjdAXukVIYp0IjMZokmWIrp0+fZtq0aSQlJXHr1i0OHjxY1iEJIYQQQghx35BkSj7Onz/P2rVr0Wg0BAQEFKrPt99+C4C7uzsTJ07Mcb5ly5YMGTIEgNDQUE6dOmW9gIUoYwa9kSsXb1tlrOibyaTq9FYZS2R34MABAgICSElJAcDf358+ffqUcVRCCCGEEELcPySZkgeTycSMGTMwGAyMHj2aBx54oMA+YWFhXLt2DYDevXtjb2+fa7uBAwdajrdv326dgIUoB0xmM7HRd6w2XvS/1htLZNi2bRsLFy7EYDCgUql499136devX1mHJYQQQgghxH1FdnfMw/r16zl9+jR+fn6MHTu2UH2OHTtmOe7QoUOe7Zo2bYqTkxM6nS5bHyHKi+TkZK5fv47RaCxSv8aNmpKcpEOvt86MkqSEu8TExBAVFVVgW5VKRZ06dXB1dbXKtSsas9nMTz/9xLp16wCwt7dn6tSptG3btowjE0IIIcT9JigoiKlTpwIFVznNz8svv8zhw4fp2LEj3333nTVDpHv37kRFRTFw4EA+/PBDq44tBEgyJVe3bt3i008/BWDGjBk4ODgUqt/Vq1ctx/nNZFGr1dSuXZsLFy4QFhZWsmCFsIHIyEji4uKK3M9sNqNA8X/s3XlYVGX7wPHvbCCLLCJuuOUSLrjlkppL7mmWu5VkYrm8pVlm5ZIppqlpb1ZqWvm6r5VrpplrZqmI4gIKJK5gIqIgO7P9/uA3E8gM6yAg9+e6uBrOOc8590w6ztznee4bo6HgxWczUygVpKamEheXtxosKpWKRo0a2eTaj5stW7awceNGAMqXL8+MGTPw9vYu5qiEEEKI/Dt58iSvvfZatu0qlYry5cvj7OxMtWrV8PHxoXXr1nTq1Am1Wr72CCFsS95VLJg9ezZJSUn06tWLzp0753nc7du3zY8rV66c47FVqlQhNDSU+/fvk56ejp2dXY7HJycn57jfVPtACFuoXr06er0+3zNTtFot7hWduBVZ+AK0AJ6VnVEoUnFzc8v1WJVKVeC7ImVBu3bt2LlzJw4ODsyaNYsaNWoUd0hCCCGETen1euLi4oiLiyMyMpKAgABWrlxJ5cqVGTVqFMOHD0ehUBR3mEKIx0S+kimWMsC2oFAoWLNmTZGcO78OHDjAgQMHcHR0ZNq0afkam5SUZH7s6OiY47EODg5ZxuWWTGnRokW+YhGiMFxcXGjSpEm+xxkMBrxquBMeUvgitGq1Es/KLjg4VpQkiQ3UqlWLWbNm4e7ujqenZ3GHI4QQQtjEK6+8wrBhw8y/JycnEx8fz8WLF/nzzz85deoU0dHRfPrpp/z+++8sWbIky+dwUbxsvbQns0OHDhXZuYWAfCZTAgICbJ7NNRqNJSZDnJiYyOzZswGYMGECVapUydf4tLQ082ONRpPjsZmTJ5nHCVGaKZVK6jeqzLFDl9Gm529Wy8PqNaiESi01sk0MWi1GgwFDWjqp0dHoU1NROzhgX7kSSjs7FCoVykxTmB88eEBERESWROyTTz5ZHKELIYR4BHQ6HcHBwfj4+OS4pCWvx5UWHh4eFv9969y5M2+++Sbnz5/ngw8+4Nq1axw7dozJkyfz1VdflZjvH0KI0ivf76BGY861EExvTDkdl5djisOXX37J7du38fb2Zvjw4fken7l7j1artdrNByA9Pd3iOGuCgoJy3J+SkkL79u3zEKUQRcveXo1P82oEBdws8DmUSgVPta2JRqOyYWSlk0Gnw5CWRuzJAO7+8SfJN25izLT8SqFS4fREbTw7dsC9dUtU9vbE3r/PjBkzuH37NjNnzqRZs2bF9wSEEEIUOZ1Ox/Tp0zlw4ADdu3dnzpw5FhMleT3ucdK0aVN++OEHBgwYQFRUFPv27eO3336jV69exR2aEKKUy9e7Z2hoqNV9CQkJTJs2jf3791OtWjWGDRtGu3btqFWrFg4ODqSkpHD9+nWOHz/Opk2biIqKomfPnsydOxdnZ+dCP5HCOn/+PBs2bEChUODv71+gf1icnJzMj5OTk3NMkmSucZJ5nDW5LRsSoqTQ2Klo27kOVy/HEncv51o/1rRsVwsPT+cyf9dIn5ZG8o2bXFu1lpRbtyweY9TrSbwcQeLlCKIPHaHWa77E67QkJiai0+n4888/JZkihBCPscwJEsD834cTJXk97nHk6urK7Nmzef311wH47rvvrCZT9u/fz/bt2zl//jxxcXE4OTlRr149evXqxcsvv5xtaX5gYCDDhw/HYDDQq1cvvv76a4vnjY6O5oUXXiA+Pp569eqxbdu2bN8VLl68yPr16wkICODOnTtoNBq8vLzo1KkTfn5+VKxY0eK5MxfkXbt2La1bt2br1q3s3LmTiIgI7t+/z/Dhw/noo48AzAXox48fz9tvv231dctPN5zU1FTWrl3LL7/8wo0bN1CpVHh7ezN06FD69etndVxeu/nExMSwYcMG/vzzT27cuEFiYiLOzs7UqVOHdu3a0b9/f2rWrJnn+B9+zZ5++ml2797Nli1bCA8PJzU1lerVq9O7d29ef/11+S4mLLLJO6der+fNN9/k9OnT9OnTh7lz52brgOPs7Ezjxo1p3Lgxw4cPZ9q0aezZs4f79++zZs0alMrinc6/cuVKDAYDDRo04J9//uGXX37JdkxkZKT58eHDh6lQoQIajYaePXsCZFkWFB0djbu7u9XrmYrVuru751ovRYjSxs5eTd8hTdm+4QxJiem5D8ikfsNKtOn4BBq7sj0rRZ+WxoPgEC4v/x5jHltNP4iIIOCjj2k08R2mfvABJwIDGTFiRBFHKoQQorg8nCAxeThRktfjHmfPPPMMdevWJSIiguDgYKKjo7M0jEhNTeXdd9/l8OHDWcbFxcURGBhIYGAgGzZsYMWKFVmKuLdq1YpRo0bx3XffsW/fPrZv386AAQOynMNoNDJ58mTi4+PRaDQsXLgwWyJlyZIlLF26FIPBYN6WlpZGWFgYYWFhbNy4kUWLFuXaHCMtLY2RI0dy4sSJfL9GBRUXF8f48eO5dOlSlu2m1+3gwYN88cUXBf4ztm3bNj755JNsDTfi4uI4c+YMZ86c4dSpUwWuv2IwGHjvvfeyff+7fPkyixcv5tChQ6xbty5PN8BF2WKTd82ffvqJwMBA6tWrx4IFC3L9i2Jvb89nn31GaGgogYGB/PTTTwwdOtQWoRSYadlNaGgo7733Xq7Hz5kzB8hoMWpKpmRuh3zlyhUaNGhgcaxer+fGjRsA1K1bt1BxC1ESqVRKKlR0YvBrLdn/8yVu3cy9u49SqaBlu1q06fgE9vaP9we63Bj1elJv3ybi2xV5TqRotRmzUYxGI8FffMXTC+biN2IEimJOVAshhCga1hIkJqbt/v7++Pv753pcWUiotGvXjoiICCDji/7zzz9v3vfBBx+YEylNmjTBz8+P2rVrExsby9atW9m3bx/Xrl1jxIgR7Nq1K8vM+gkTJnDs2DEuXrzInDlzaN26dZbi+WvWrOH48ePmYxs1apQlrnXr1rF48WIAPD09GTt2LM2aNSMtLY3Dhw+zdu1akpKSGDduHFu2bKFx48ZWn+PChQsJDw+nZ8+e9OvXj6pVq3Lnzp18d2jMj5kzZ3Lp0iVefPFFXnzxRdzd3fn777/5/vvviYiIYN++fSxYsCDfzT0gI5EydepUIGOm/ksvvcQzzzyDh4cHCQkJXLp0id9++61Q8X/11VcEBQXRq1cv+vXrR5UqVbh9+zYrV64kMDCQkJAQli5dyocfflio64jHj03eMXft2oVCoaB///55fhNWq9UMHDiQhQsXsnPnzmJPptjCU089ZX4cGBhInz59LB4XEhJibnWceYwQjxO1Wom7hxMDfVsQGnybC6ejiP7ngcXj6jaoRMu2NfHwdC7zM1IADOlarq1ehyE9b7N60tPTSUpKxGgEhQLKKZVEbdzCkxMnoHpolqAQQojSL7dEismBAwc4e/Ysd+/ezfU4ePwTKg0bNjQ/vnbtmvnx4cOHzV/IO3fuzDfffJPldejcuTNff/01S5cuJSoqiqVLlzJ58mTzfo1Gw+eff87AgQNJTExk8uTJrFu3DqVSSXh4OP/9738BaNmyJaNGjcoSU2xsLJ9//jkAXl5ebN68mUqVKpn3t27dmg4dOjBq1Ci0Wi0zZsxg69atVp9jeHg4b7/9NuPHjzdvyyn5YgvBwcF8+OGHvPHGG+ZtPj4+9OrVi1dffZWQkBDWrVvHkCFDqF+/fp7PGx0dzSeffAJApUqVWLNmTZab1wBPP/00fn5+5ln/BREUFMSkSZMYM2aMeVvjxo3p2LEjgwcPJiwsjK1btzJx4sRcm4yIssUmtyyvXr0KZLwB5Ee1atWyjC9O33zzjXkanbWfzFP2Dh48SFhYGIGBgeZtdevW5YknngBgz549Vrv0bN++3fy4R48eRfSMhCh+SqUCO3s1jZtVY4hfS954pwMvDGnKs72epGvvBrzk14rREzvS44WGVK7qIokUMmalxIeEkHT1Wp6O12rT/39GSkZxb2dnZ+zt7UkICycx4grGTNOFhRBCPB6Cg4NzTaSY5JZIMTlw4ADBwcGFCavEc3NzMz9+8ODfGzwbNmwAMmbPW0sojRs3zpwI+Omnn7I0k4CM7wHvv/8+kHFTdcWKFaSnp/P++++Tnp6Os7MzCxYsyFbaYOvWraSmpgIwderULIkUk/bt2zNkyBAg4//9+fPnrT7HOnXq8NZbb1l/EYpAo0aNzPVoMnN0dDQnQwwGA1u2bMnXedevX29e2jNnzpxsiZTM8tuFNbMmTZpkSaSY2NnZ4evrC2QsKTLNahLCxCbJlKSkJADu3buXr3Gm403jHwemN5L79+/zxRdfZNt/4cIFfvzxRyAjO920adNHGp8QxUGlVmJnp8bVzYH6jSrT4umaNG9TA69a7jg42mFnp0ahLNvFZk0MWh13j/2V5+M1Gg1qtRqFQkH58uXRaP6twRRz9FieZ7cIIYQoPXx8fOjevbtNz9m9e3d8fHxses6SJnMRUdP3D51Ox6lTpwDo2LGjxWQGgEqlYtCgQUBGIiYkJCTbMa+++iodOnQA4Ouvv2bSpEmEhYUBMH369CxLf0xMy3/c3Nzo2rWr1dgzz+I3jbGkd+/ej7wWZb9+/aw2DfDx8TG3rs4pbkuOHDkCQO3atXOtFVMYffv2tbov85KszPUzhQAbJVNMxZv27NmTr3Gm4zMXfyrtBg4cSPPmzQFYvXo1kyZN4q+//uLcuXP873//w8/Pz9w2uSDrBoV4HJT1Lj05MhpIvJKf2XoZSRQXF5dsd9LyOrtFCCFE6aJWq5kzZ47NEiplpU2yaZk9YK55cvPmTfPMkNxucmbe//fff2fbr1AomDdvHm5ubmi1WvPSoV69emUrSvvweXx8fFCprM/QbdCggblobXh4uNXjTJ16HqXcknBNmjQBMmpK5rV2i1arNb82LVu2LFyAuTCtLLAk82ymxMTEIo1DlD42ecfs2LEjGzZs4MyZMyxZsiTLGj1rvvnmG06fPo1CoaBTp062CKNEUKvVLF26lNGjR3Px4kV2797N7t27sxzj6OjIwoULH/vsvxAl2YMHD7h+/XqRFmTLLxcXF6qWdyH1XqzVY4zGjA8YdhoNPJST0umyLunR/XMLXVoaVyMjS+UHAJVKRa1atXBxcSnuUIQQosQxJVSAPC/5saSsJFIgY+a4ienflvj4ePO2ChUq5Dje09PT/DguznJx/UqVKjFhwgTz8hYXFxdmzZpl9Zym6+d2bZVKhbu7O7dv384S88OK499MDw+PPO03GAw8ePAgx46nJvHx8RiNRiDr614UHu5Cm1nmG4AGWTotHmKTd8033niDbdu2kZqaytKlSwkMDGTkyJE8/fTTWf5wpqamcvLkySwVrR0cHLIUK3ocVKxYkS1btrBlyxZ++eUXrl69SmpqKlWqVKFjx46MGDEiS0s1IcSjFxkZSWys9aRFcVCr1ejLOWAwGC3uNxqNpKamotfrMRgMeWirbsSg05OUlGT1Q19Jp1KpsnU9EEIIkaGwCZWylEgBsrTutTQbwRYzZ9PT07PUBnnw4AEXLlzI9eaxrWbtPuolPkKUZTZ556xWrRoLFy7k3XffRa/Xc/LkSU6ePIlSqaRixYqUK1eO1NRU7t69a87oGY1G1Go1CxYsoGrVqrYIo8jNnz+f+fPn5+lYOzs7hg8fzvDhw4s4KiFEQVSvXh29Xl+iZqaUK1cOtYMDSrUKHrr7YTAYSU1NQa83/P/vehQKBTl99lJqNKjsNDg4OJjv7pQmKpXK4vpyIYQQ/1Kr1fj7++epa09mFStWxN/fv8wkUgD++uvfmmSmpSOurq7mbbndZImJiTE/zrz8I7NFixaZ66Q4OzuTmJjItGnT+Pnnny3OyHB1dSUmJibXa+v1evPMmswxF4RCocBoNOY60yLzsqicxMbG5rhUxvTclEplnmfOuLq6muPM/LoLUZLY7N2ze/furFmzho8++sjcakyv1xMdHW3+i5DZE088waeffiqtgYUQxcLFxcW8hrck0aem4uzlReo//7b4Mxj0JCUlmBMpdnZ2ODs7kW2dz0OcatVCZWdnLvwmhBDi8aPT6fD3989XIgUyuvz4+/uXmZkpx44dM3cQbdasmbnQbI0aNcw3fnPqkgMZjSRMLLX4DQgIYPXq1QD06dOHoUOHMnLkSGJiYpg5cyZff/11tjH169cnJiaGkJAQ9Hq91bopYWFh5k6hhf133cnJicTERBISEqweExcXl+dZrcHBwbRq1crqftPrVqdOnRzrwmSm0WioX78+4eHhnD59Ok9jhHjUbDoPrGXLluzZs4clS5bQv39/6tatS/ny5VEqlZQvX566desyYMAAli5dyi+//CKJFCGEsMClYQPzY71ez4MH/yZS7O3t/79oXu7TgctnOo8QQojHj06nY/r06QWumXLgwAGmT5+OTqezcWQlS3x8PDNmzDD/nrkNrlqtpnXr1gD88ccfVmdBGAwGtm7dCmTckGncuHGW/QkJCUyePBmDwUDlypXx9/enXbt2+Pn5AbBv3z527NiR7bzt2rUDMuq5HD582OpzMHUDzTymoLy8vABybIW9d+/ePM9q3bVrl9VjQ0JCzAVz8xu3qbvRtWvX+P333/M1VohHweaL6pRKJd27d2f+/Pn88ssvnDp1ipCQEE6dOsUvv/zCvHnz6Natm6znE0IIC1TlylGpy7OgUKDT6Xjw4IF5Gq6DgwNOTk55Oo9CpaLSs51Q/X/lfyGEEI+XwiZSTB73hMr58+cZOnQoUVFRQEbr4Ie7IPn6+gKQlpbGxx9/bHEJ8DfffGNOCgwePDhb3bJPPvmEW7duoVAomD9/vnkpznvvvWeeSTJ79mxu3bqVZdygQYPMNSbnzp1rcYbR8ePH+eGHH4CMzjm5dR3KTZs2bQA4e3f4sBoAACAASURBVPYsZ8+ezbb/2rVrfPnll3k+X0hICGvWrMm2PSUlhZkzZwIZ3xEzt3fOC19fXxwcHICM1tJXrlyxeuzt27et7hOiqDz+c/qEEKKUsfesSMVOHfh7x793ehwdHXOsNv+wKj27o3G1vJ5bCCFE6WarRIqJ6TylcclPbGxsllbBKSkpxMXFcenSJf78808CAgLM+zp16mSx/mGXLl3o2bMnv/32G4cPH+aVV17Bz8+PWrVqcffuXbZv387evXuBjFkd48aNyzJ+79697Nq1C4BXX32V9u3bm/fZ2dmxcOFCBg8eTGJiIpMnT2bNmjXmG8seHh68//77zJkzh6ioKAYOHMjYsWNp0qQJ6enpHDlyhNWrV6PT6dBoNOYuQYUxZMgQNm7ciF6vZ+zYsbz99ts0b948S7MQNzc3lEol9+7dy/V8Pj4+zJs3j0uXLvHiiy/i6urK5cuXWbFihbm9sa+vb76XJ1WqVIkZM2YwdepU7ty5w6BBg3j55Zfp0KEDFSpUIDExkUuXLrF//34A1q1bl/8XQ4hCKF3vlkIIUQaoypWjxqCBxIZc5N7ff+Pk5JyHzj3/cnriCar27YOqnMxKEUKIx1FwcLDNEikmBw4c4OWXX6Z58+Y2PW9R27RpE5s2bcrxmCpVqjB69Gh8fX2tds1ZuHAhWq2Ww4cPc+7cOSZOnJjtmNq1a7NixYr/X26bITo6Gn9/fwDq1q3LBx98kG1cgwYNmDhxIgsWLCAgIIBVq1Zl6WY6fPhw4uPjWbp0KdHR0RYTJk5OTixatCjb8qKC8Pb2ZuLEiXz++efExcUxe/bsLPurVq3KsmXLGD16dJ7ON2vWLD766CN27NhhcSlTjx49mDx5coFiHThwIHq9ntmzZ5OcnMzKlStZuXJltuNMs22EeJSKJJly9uxZjh07xpUrV4iPj0en02Wb+nXv3j20Wi329vZWq2ELIURZpXZypOmUDwn7ajGpNyPzPM65Xj3qvTUW1f9PixVCCPH48fHxoXv37nlKqFSsWDFPxWm7d++Oj4+PLcIrNiqVCicnJ8qXL4+Xlxc+Pj60adOGTp065Vr4tFy5cixfvpz9+/ezbds2Lly4QFxcHE5OTtSrV4+ePXvyyiuvZLm5YTQamTZtGnFxcWg0GhYuXIi9leW1I0eO5MiRIwQEBPDll1/SoUMHvL29zfvHjx9P165dWb9+PSdPniQmJga1Wo2XlxedO3fGz8+PihUr2uaFAkaPHk39+vVZs2YNwcHBpKWlUbVqVbp168aoUaOoUKFCns/l5ubG5s2bWb16NXv27CEyMhKlUom3tzdDhw6lf//+hYp1yJAhdOzYkfXr13Ps2DEiIyNJTk7GxcWFunXr0r59ewYMGFCoawhREAqjDftlRkRE8NFHH3Hu3DnzNqPRiEKhyNLXHeCzzz5j9erVuLu7c/To0VI3pbCkSU5OpkWLFgAEBQXh6OhYzBEJIfLDYDCwc+dOevbsaa6LYtTr0aem8s+eX4nefxBDerrV8cpy5aj6XE8q9+yOqlw5FFKXSgghHmt5WerTvXt3/P398ff3z/W40rjERwghipPNkilnz55l5MiRpKamZqvmbCmZEhUVRbdu3VAoFCxfvpzOnTvbIowyS5IpQpReWq2Wr7/+mt9//50mTZowc+bMLHe+9Glp6JOTif3rBAl/XyYl6haG9DSU5crh4FWN8k8+iUe7p1GVKycFZ4UQogzJKaGSOUGS1+OEEELknU3eNZOTkxk/fjwpKSmo1WrGjBnDiy++SGhoKO+++67FMaapdyEhIRw7dkySKUKIMik1NZX58+dz5swZIKM1YmJiYpbptSp7e1T29lTu0Y1K3bpkbDQa4f/XfStUKpQazSOPXQghRPFSq9XMmTMHIEui5OEESV6PE0IIkXc2eefcuHEjd+/eRalUsmTJEp599lkALl++nOO4li1bEhwcnGOPcyGEeFw9ePCATz75xNyF4Mknn2TGjBm4uLhYPF6ZjyK0QgghyoaHEyXWEiR5PU4IIUTe2OTd8/DhwygUCrp27WpOpORFnTp1ALhx44YtwhBCiFIjJiYGf39/bt68CUCLFi2YMmUKDlI4VgghRD6ZEiUvv/wyPj4+VhMkeT1OCCFE7mzyDnrlyhUAOnbsmK9xrq6uACQkJNgiDCGEKBVu3rzJzJkzzd0VOnXqxDvvvIOmAEt1tDoDOr3B4j61SolGLYVohRCiLFCr1Xlqa5zX44QQQuTMJskUUzLE3d09X+N0Oh1Arq3KhBDicaHVarMkUvr27cuoUaNQFrD7jk5vYMHaU8QlpmXZ7uZsz4evtZZkihBCCCGEEEXAJp+y3dzcAIiLi8vXuMjISCD/SRghhCitNBoNb775JiqVildffZXRo0cXOJFiEpeYxr0HqVl+Hk6uCCGEEEIIIWzHJsmUWrVqARktefPj6NGjKBQKGjZsaIswhBCiVGjdujVLly5l6NChKP6/I48QQgghhBCi9LBJMqVjx44YjUZ+/fVXoqOj8zTmjz/+MLcClbbIQojH2Z49e4iIiMiyrVq1asUUjRBCCCGEEKKwbJJMeemll3ByciI1NZU333yTmJiYHI//66+/eP/99wHw8PCgf//+tghDCCFKFKPRyPr161m+fDn+/v7cunWruEMSQgghhBBC2IBNCtC6u7vz0UcfMW3aNC5dukTv3r15/vnnsxSW3b59O7dv3+bPP//k9OnTGI1GlEolc+bMwc7OzhZhCCFEiaHX61m2bBm//fabeVtqamqBzpVTxx6tzkCaVp9te5pWj1ZnAHTZ9kmXH2HQajHqsv+5AVCoVSgL0FlKCCGEEKIsURiNRqOtTrZmzRoWLlyITqfLsQ6A0WhErVbj7+/P4MGDbXX5Mi05OZkWLVoAGbVrHB0dizkiIcqu9PR0Pv/8c06cOAFA5cqVmTVrVoGX9qSk6Sx27IGMpElSitbiOCcHDfaarN3STF1+HOxtkksXpZQ+JZXQBf9FG5+1cLzG1Y0GH05C5VCumCITQgghhCgdbPppesSIETz11FMsXbqUo0ePYjBkv5OqUCjo1KkT7777Lo0aNbLl5YUQotglJSXx6aefEhwcDEDt2rXx9/enQoUKhTqvqWNPvmJJ0VpNtAihjY8j/d794g5DCCGEEKJUsvmtySZNmrB8+XIePHjAmTNniIqKIiEhAUdHRypXrkzr1q0L/aVCCCFKovv37+Pv78/Vq1cBaNy4MR999BHOzs42v5bBYESvt7xMw0SlUqFUSrcgIYQQQgghbK3I5nm7uLjw7LPPFtXphRCixFmyZIk5kfL000/z/vvvY29vXyTX0hv0Fmf/ZaEApVKW8wghhBBCCGFrNvmUbepQ4eHhka8vDunp6dy9exeQNqFCiNLvP//5D1evXqV58+aMGzcuSxFuW1MpVZBLxSuVsuiuL4QQQgghRFlmk2RK165dUSqVLF68mG7duuV53MmTJxk9ejRKpZKLFy/aIhQhhCg2np6e/Pe//8XNzS3HItwF4eZsOVFdkAK0QgghhBBCiMKx2fzvwjQFsmFDISGEeGROnDhBXFwczz33nHmbu7u7za+jVin58LXWFvdpdQamLP0jW0LFyUHDZ+M6orbQAlmtkrbIZUFO7Y8NWi2GtPTs29PSMWitFy2WtslCCCGEEBlkMb0QQhTA/v37Wbp0KUajEWdnZzp06FBk19KolWgsJEUy6LDXqLIlU+w1KtRqpbRALsOMOr3F9seQkTTRJSVl265LSuLC1Oko7e2y7TO1TUaSKUIIIYQQFOvtyYSEBADKlStXnGEIIUSeGY1Gtm7dyuLFizEYDGg0GhwcHIo7LCEsMrU/fvjHUiLFRJeUZHGMpaSMEEKIR8fb2xtvb28WL15c4HNs27bNfJ7IyMhs+6dMmYK3tzddu3YtTKhClAnFesvyt99+A6Bq1arFGYYQQuSJwWBg1apV7Ny5EwBnZ2f8/f158sknizkyIXJmNBrR63WFOodSp0Nv0PN3aCjVqlXDxcXFRtEJIUTenTx5ktdeey1fY6ZOnYqfn1/RBCQea8OHDycgIMDiPo1Gg4uLC/Xr16dr164MHjwYJyenRxyhKE75TqYcOHCAgwcPWty3bt06Dhw4kOs5kpOTCQ0N5caNGygUClq1apXfMIQQ4pHS6XR8/fXXHDlyBMjoXjZ79my8qlbGkJ5qcYxCqUKhliURovjp9XoMhsLVJzMYjBiNRu7fv4/BYKBRo0Y2ik4IIcou05f1Nm3asG7duuIOR+SDVqslNjaW2NhYTpw4werVq/nuu++oX79+cYcmHpF8J1NCQ0PZvn17tk4VRqORkydP5utcRqMRBwcHyRQLIUq01NRUPvvsM06fPg2Al5cXn3zyCZ6enhjSU4ne9l/0SVmXQKic3Kg8cBIKJJkiil9Gm+7CJVOUSgUKhQJ3d3eqVatmm8CEEAXy/fffs3//fpues0ePHowePdqm5yxqr7zyCsOGDcv1OE9Pz0cQTekwcOBABg4cWNxhlEo///xzlt/T0tK4fv06W7ZsISAggFu3bjF27Fj27t2Lvb10TywLCrzMx1IHnvx05Slfvjxt27Zl3Lhx1KlTp6BhCCFEkdu1a5c5kVK/fn1mzpyZZYmDPikOfeL94grPYrtjaYEsIKNorCXWCtACqJ2crBagVSlVNGjQwKYxCiHyb//+/Vy5csXm5yxtyRQPDw9ZaiseGUt/1po0acLzzz+Pn58fJ06cICoqit9++40XXnihGCIUj1q+kykjRoxgwIAB5t+NRiPdu3dHoVDwySef0L59+xzHK5VKHB0dcXV1zX+0QghRDAYOHEhoaCh6vZ4pU6aUqIKzObVNlhbIZZtCrcrovmOBQavlwtTp2RIqaicnmsyfg9LK8jSFWmXzOIUQQojSTKFQMHLkSE6cOAFAcHCwJFPKiHwnU8qXL0/58uWzbTcajVSoUAEvLy+bBCaEECWFWq1m8uTJKJVKNCWsLWzObZNFWabUaHJsY6y0t4OHkilKezuUag0qB+myJ4R4vHl7ewMwfvx43n77bY4ePcq6deu4ePEiCQkJVK1alW7dujF27NhcbwKnp6ezefNm9u3bx+XLl0lKSsLNzY2mTZsycOBAunfvnm3M4sWLWbJkifn3gIAAc0wmXl5eHDp0yOp1g4KCWLVqFWfOnCEuLg5PT0+eeeYZ3nzzTavfybZt28bUqVMBOHjwINWrV8/xuVlz8eJF1q9fT0BAAHfu3EGj0eDl5UWnTp3w8/OjYsWKFsdlLiC8du1ann76aXbv3s2WLVsIDw8nNTWV6tWr07t3b15//XUcHR1zjEOv17Nr1y5+/fVXQkJCiIuLw8nJiTp16tCzZ09eeeWVR9I5NvPrmJ6eXuTXEyWDTbr5mArSenh42OJ0QghRrMLDw9m3bx9vvfXW/9eaQNa+ClFC6PUGDAYjGo3MkhFC2MaXX37JsmXLsmy7du0a//vf/9i1axdr1qyhbt26FsfevHmTUaNGce3atSzbY2JiOHjwIAcPHqRr164sWrTIpl/q165dy/z589Hr9eZtt27d4scff2T//v2sW7euyJZALVmyhKVLl2IwGMzb0tLSCAsLIywsjI0bN7Jo0SI6d+6c43kMBgPvvfcev/zyS5btly9fZvHixRw6dIh169ZZ7ZBz69Yt3nzzTUJDQ7Nsj4uL48yZM5w5c4ZNmzbx7bff8sQTTxTw2eZNVFSU+bF0qi07bJJMkdkoQojHRVBQEPPmzSM1NRWFQsG4ceOyFdwWQhQfvc6ATmeQZIoQwiYOHz5MSEgI9erVY/To0dSrV4/79++zc+dOfv75Z2JiYhg1ahQ///wzzs7OWcYmJiYyYsQI8xfpPn360L9/fzw8PLh69SqrVq0iJCSEQ4cO8eGHH/L111+bxw4bNoxevXoxdepUgoOD8fHxYd68eVnOb2027LFjxzh37hyNGjXitddeo27duiQlJbFr1y62bt1KXFwc06ZN46effrLxq5XRvXXx4sVARmHfsWPH0qxZM9LS0jh8+DBr164lKSmJcePGsWXLFho3bmz1XF999RVBQUH06tWLfv36UaVKFW7fvs3KlSsJDAwkJCSEpUuX8uGHH2Ybe//+fYYNG8Y///yDvb09Q4cOpVWrVnh5eZGUlMSxY8dYv349169fZ/To0Wzfvt3i6gpbMBqNrF69GshY8tO1a9ciuY4oeWySTAEIDAzEaDRSvXr1PGXjbt26RVRUlLRGFkKUGEePHuXLL79Ep9OhUCioV68eCoUCo16LMdOdn8yMOi1GXfbpnEZdOkadFoOFMSBtk4UoCL3ewI2r97gdFc/THeugsZOEihBlWWxsLOHh4bkel9MMjZCQEJo0acK6deuy1ETr2LEjTz75JP/973+5desWy5cv5/33388ydunSpeZEysSJE/nPf/5j3ufj48Nzzz3HmDFj+Ouvv9i3bx9Hjhzh2WefBTJm9Ht4eJiXsTg6OuZ5JsnZs2fp0qULixcvzpJwadu2LWq1mi1btnDhwgVCQkJyTGbkV2xsLJ9//jmQcTN98+bNVKpUyby/devWdOjQgVGjRqHVapkxYwZbt261er6goCAmTZrEmDFjzNsaN25Mx44dGTx4MGFhYWzdupWJEydmSyzNmTOHf/75h+rVq7N27dpsN/fbtm1Lnz598PX15ebNm6xYsYKJEycW6vk//GctPT2d69ev8+OPP3L8+HEARo0aRb169Qp1HVF62CSZcvz4cUaOHIlCoWDr1q15SqY8ePCA4cOHo1AoWL9+PS1btrRFKEIIUSC7d+/m+++/x2g0olarmTRpEs888wwARr3eYvtjyEiaGFKzd0UxpCZxa910FOrsXVGkbbIQBaPXGQg4dpW42GRatKkpyRQhyrhNmzaxadOmXI8LCwvLcf/s2bMtFpcfNWoUu3fvJiwsjJ9++ol33nnH/KU+PT3dPPOjYcOGjB07Ntt4jUbD3Llz6dGjB1qtlg0bNpiTKYVRrlw55s6da3HmysiRI9myZQuQcbPblsmUrVu3kpqaCsDUqVOzJFJM2rdvz5AhQ9i8eTPBwcGcP3+epk2bWjxfkyZNsiRSTOzs7PD19WXGjBnExcURERGRpZNcZGQke/fuBWDmzJlWV0k0atSIYcOGsWLFCrZt21boZEpORWWbNm3KmDFj6NGjR6GuIUoXm1QtNPW5b9iwIY0aNcrTmAYNGpj/cv/666+2CEMIIfLNaDSyYcMGvvvuO4xGIw4ODsycOdOcSDExtT82/egS7pF6/w5pCXFotVqLP2kJcaTev4Mu4V6WsZaSMkI8ahpXN+wquGf5sdZKuST4d1bKA1JTdQQF3ECbbnnGmBBC5FWDBg1o2LChxX1KpdLcxfT+/ftcunTJvO/ChQs8ePAAgAEDBlhdEly1alU6dOgAwKlTp7LUOCmo9u3bU6FCBYv7nnjiCfNsl8jIyEJfKzPT7As3N7ccl7IMHTo02xhL+vbta3Vf5u+UDz+P33//Hb1ej5OTEx07dswx5tatMzoe3rlzh1u3buV4bGEEBwezcePGXBN34vFik5kpZ8+eRaFQmN8o8qpDhw6EhIQQFBRkizCEECJf9Ho93377rTmh6+rqir+/v9Uicw+PNRiMeb6OWm2zVZVCFFpObZNLavtj06wUk/OBkTI7RYgyztSJpzB8fHxy3N+kSRPz47///ts8y+Ly5cvm7dZmXpg0a9aMw4cPk5KSws2bN6ldu3bBA4Zci6m6urqSnJxMUlL2mbOF8ffffwMZr5mpQL8lDRo0wN7enrS0tByXYeX0PNzc/k3uJyYmZtkXHBwMQFJSUpYZK7m5e/cu1apVy/PxD3s4UaLX64mNjSUgIICvv/6av/76i2HDhvHtt99KGYsywiYzU27cuAFAnTp18jXO9Bfo5s2btghDCCHyJSQkxJxIqVSpEp999lmeEikAKpUKpVKRp5+cPnAIURyUmoz2x5Z+lCWs/TdknZViIrNThBC2kFs30swzQOLj4y0+zu0cmdsEZx5XUJaWJGWmVGZ8xcvcbccWTLFbmxVjolKpcHd3zzLGkpy6G2We6fPw84iNjc01VktSUlIKNM4alUpFpUqV6Nu3L5s2baJChQokJiby4YcfotPpbHotUTLZ5Fapae1cbn+xH2b6C2TrrKkQQuRF06ZNGTlyJIcOHcLf3z9f7d0VCoXVKvtCCNt6eFaKicxOEUKUBGWt619xP1/TUilPT09WrlyZ53HVq1cvqpDw8PDgxRdfZPXq1URFRXHixIl8r9oQpY9Nkinly5cnLi4u31lC0/HWeocLIURRGzBgAH369MHe3j7H41ROlmtJWCtAC6As52S1AK0QIm8szUoxMc1Okc4+QoiCyu37y71798yPXV1dLT6+e/cuNWrUsHqOu3fvWhxX2ri6uhITE5Pra6bX67l//755jK2ZlgAlJiZSr14980yc4pZ52VJ4eLgkU8oAmyRTvLy8iIuL4+TJkwwbNizP4wICAgCoUqWKLcIQQogc3b59m2XLlvHOO+9kmaKaWyJFoVRReaDl+hJGnZZb66ZnS6goyzlR7bU5KFSWZ68olPLFT4i8sDYrxURmpwghCsNUf8OaCxcumB/Xr1/f/Dhz+9vz58/TokULq+c4f/48kDGLvyhnRxS1+vXrExMTQ0hICHq93uoy5rCwMNLS0oCc21IXVKNGjdi9ezcpKSlcvHgx17o3j0rm4sKyzKdssEka7+mnn8ZoNHLw4MEsxZhyEh4ezoEDB1AoFLRt29YWYQghhFVXr15l8uTJBAUF4e/vn691swq1BqVdOYs/CrXG4uwThdoOhSrncUKInJlmpfxzMx6dVmexa1ZCQgqnT1wjNTW9uMMVQpRCoaGhhIaGWtxnNBrZsWMHkDEbInOx0yZNmuDi4gLAjh07MBotF6WPjo7mjz/+ADI6yzxckN50Qyc9veS/h7Vr1w7I6Gx0+PBhq8f9+OOP2cbYUpcuXcxLjdasWWPz8xdU5sRc1apVizES8ajYJJkyZMgQVCoVer2eMWPG5JpQ+fvvv3nzzTfR6/UolUqGDBliizCEEMKikJAQpk2bZp5y2rJlyxyLngkhSgbTrBS9QY/BYMBoMFr8OXPiOulpUohWCFEwM2bMMNeAzOx///ufOdEyaNAg7Oz+vXliZ2fH4MGDAbh48SIrVqzINl6r1TJt2jS0Wi0Avr6+2Y7x9PQEMhpyWEvIlBSDBg0yf36aO3duluVLJsePH+eHH34AMrr+5NbpqCDq1KnDc889B8CuXbtYu3ZtjsffvHmT3bt32zyOzMLCwvjll18A0Gg0tG/fvkivJ0oGmyzzqV27Nr6+vqxdu5Z//vmHgQMH8sILL9ClSxfq1q2Lo6MjycnJREREcOjQIXbv3o1Wq0WhUPDyyy9nmSYnhBC2dPLkSRYuXGi+4zNy5EgGDBhQzFEJIXKTuVaKSqkCIxix/EUjPU3PucBI2krtFCHKlNjY2Bxb75o4OztbbYnbuHFjzp07x5AhQ3jjjTeoV68e8fHx7Ny5k507dwIZJQneeuutbGPHjRvHvn37iIqK4vPPPyc0NJR+/frh4eHB1atXWbVqlXm2Qq9evXj22WezneOpp55i27ZtxMbGMm/ePF588UXKly8PgFqtxsvLK68vR5Hz8PDg/fffZ86cOURFRTFw4EDGjh1LkyZNSE9P58iRI6xevRqdTodGo+GTTz4pslj8/f0JDg7m5s2bfPrpp+zfv59+/fpRr149NBoNcXFxhIWF8ccff3DixAl69OhB3759C3XNh/+sGQwG7t27x4kTJ1i/fr15adPrr7+er6YGovSySTIFYPLkydy8eZPDhw+Tnp7Otm3b2LZtm8VjTVnXrl27Mm3aNFuFIIQQWezfv5+lS5diMBhQKpVMmDCBrl27FndYQog8yFwrRaFUoFbm/JHlQmAkT0ntFCHKlE2bNrFp06Zcj+vWrRvffPONxX1dunShY8eOLF++nMmTJ2fb7+npyYoVK3B2ds62z9nZmTVr1jBq1CiuXbvG7t27Lc6A6NKlCwsWLLB4/T59+vDtt99y8+ZN1qxZk2XZipeXF4cOHcr1+T1Kw4cPJz4+nqVLlxIdHW0xYeLk5MSiRYto3LhxkcXh5ubGpk2bePfddwkMDCQgIMBcj9MSWzQ8eeGFF3Lcr1Ao8PX15d133y30tUTpYLNkikqlYtmyZXz//fesWLEix57irq6ujBkzhjfeeMNWlxdCCDOj0ci2bdvMH0js7OyYPHkyrVu3LubIhBB5kVMHH2uks48QoqAmTpxI8+bNWb9+PRcvXiQxMZGqVavSrVs3xowZg7u7u9WxNWrU4Oeff2bTpk3s27ePiIgIkpKScHNzo0mTJgwcOJAePXpYHe/k5MTmzZv59ttv+fPPP7l161a+6roVh/Hjx9O1a1fWr1/PyZMniYmJMc+i6dy5M35+flSsWLHI4/D09GTDhg0cOXKE3bt3c/bsWe7evYtOp6N8+fLUqlWLFi1a0LVr1yL5DKhUKnFycsLLy4unnnqKQYMGlZhiuOLRUBiLYHFecnIyR48e5fTp00RHR5OYmIizszOVK1emVatWdOrUCQcHB1tftkxLTk42VxEPCgrC0dGxmCMSovjcv3+f8ePHk5CQgJOTEzNmzKBhw4ZFci1DeirR2/6LPikuy3aVkxuVB05CaSe1WYTIr/Q0HT+tO52vZApAuXJqRrzVHqfyOXfoEqK0Gzp0KFeuXLHpOevUqWOudVEWeHt7AxmJgbfffruYoxFClEY2m5mSmaOjI88995y5MJAQQjxK7u7ufPzxx3z11VdMmTKFWrVqFdm1cmqbLO2Phci/gsxKMZHZKUIIIYR4VIokmSKEEI+a0Wg0t8kDaNCgAUuWLEGlKtovVAq1BgXS5lgIW8lcK6UgzgdG0kJqp4jHXI8e06woHQAAIABJREFUPdi/f7/NzymEECLvJJkihCj1EhISmD9/PkOGDKF58+bm7UWdSBFC2FZhZqWYyOwUURaMHj2a0aNHF3cYQghRpimLOwAhhCiMu3fvMmXKFC5cuMDcuXO5ceNGcYckhCigws5KMTkfGEl6ms4GEQkhhBBCWJavmSmvvfYakNH2KXPbLtP2gnr4fEIIkReRkZHMnDmTmJgYAFq1akXVqlWLOSohREEYjUb0egM9+jay2fl0OgNqtdw3EkIIIYTt5SuZEhAQkKUmQW7b8+LhOgdCCJEXf//9N7NmzeLBg4zlAH369GH06NGytEeIUkqhUODgaIeDo11xhyKEKAPCwsKKOwQhRCmX75op1pIfRdBhWQghLAoKCmLevHmkpqYCMGzYMF566SVJzAohhBBCCCEeiXwlU0JDQ/O1XQghbO2PP/5g0aJF6HQ6FAoF//nPf+jdu3dxhyWEEEIIIYQoQ6SbjxCi1DAYDOzevRudTodarWbSpEk888wzxR2WEEIIIYQQooyRqmxCiFJDqVQyffp0vL29mTlzpiRShBBCCCGEEMVCYZRiJ4+F5ORkWrRoAWTUk3B0dCzmiISwDb1ej1KpzFIPRQpXCyGEEEIIIYqTzEwRQpRYWq2Wzz//nA0bNmTZLokUIYQQQgghRHHKV82UHTt2FFUc9O/fv8jOLYQofZKTk5k3bx7nzp0DwMvLiy5duhRzVEIIIYQQQgiRz2TKlClTiuSOsEKhkGSKEMIsLi6OWbNmERERAUCjRo1o3bp1MUclhBBCCCGEEBny3c1HSqwIIYrS7du3mTlzJv/88w8Abdq04YMPPsDe3r6YIxNCCCGEEEKIDPlKpsybNy/H/QcPHuTAgQMA1K1bl3bt2lGzZk0cHBxISUnhxo0bHD9+nIiICBQKBd27d6dr164Fj14I8Vi5evUqs2bN4t69ewB069aN8ePHo1KpijkyIYQQQgghhPhXvpIpAwYMsLpvxYoVHDhwgIoVK/Lpp5/SuXNnq8f+/vvvTJ8+nQMHDvDUU08xcuTI/IQhhHgMhYSEMGfOHJKSkoCM9xs/Pz8pNiuEEEIIIYQocWzSzefs2bN88cUXODg4sH79+hwTKQCdO3dm3bp1lCtXjs8//5zz58/bIgwhRCkWFBRkTqSMHDmSkSNHSiJFCCGEEEIIUSLZJJmyfv16DAYDgwcPpnbt2nkaU7t2bQYNGoRer2fdunW2CEMIUYr5+vrSs2dP3nnnnRxnwQkhhBBClERJSUksWrSIvn370qxZM7y9vfH29mb16tUALF682LytsEznWbx4caHPZc3w4cPx9vZm+PDh2fZFRkaaY9i2bVuRxSBESZbvArSWnDlzBoVCQdOmTfM1rlmzZqxfv57Tp0/bIgwhRCmj1WrRaDRARlev8ePHF3NEQgghhChNtFot+/fv548//uD8+fPExsaSkJCAg4MDHh4eeHt707JlS5577jkqV65cZHGkp6czYsQILly4UGTXEEKULDZJpty9excAg8GQr3Gm42NjY20RhhCilDAYDKxZs4awsDBmzZolnXqEEEIIkW+//vorCxYsICoqKtu+hIQEEhISuHbtGvv27WP+/Pn06dOHiRMnUr16dZvHsnfvXnMiZeDAgQwYMAA3NzcAPD0983SOrl27EhUVxYABA5g/f77NYxRC2JZNkikuLi7ExsZy5swZ+vXrl+dxphkpzs7OtghDCFEK6HQ6lixZwqFDhwD4/vvvZUaKEEIIIfLlyy+/ZNmyZebf27Vrx7PPPsuTTz6Jq6srKSkpREdHc/LkSQ4fPsydO3fYvXs3devW5a233rJ5PCdOnAAyEidz5syx2Inw7bff5u2337bJ9cLCwmxynpxIKQYhcmaTZIqPjw9Hjhxh+/btvPzyyzRs2DDXMaGhoezYsQOFQkGTJk1sEYYQooRLS0tjwYIFnDp1CoBq1aoxZMiQYo5KCCGEeHx8//337N+/3+K+Hj16MHr06Eccke1t2rTJnEjx9PTkyy+/pFWrVhaPff7550lPT2f79u18+eWXRRbTnTt3AKhevbrFRIoQ4vFjkwK0L730EpCxZtHPz4+dO3diNBotHms0Gtm1axcjRowgPT0dgFdeecUWYQghSrDExERmzJhhTqTUq1eP+fPnF+n6ZSGEEKKs2b9/P1euXLH4Yy3JUppERUUxd+5cAMqXL8+mTZusJlJM7OzseOmll9i1axctWrQokrhM32tMteCEEI8/m8xM6dKlC/3792fHjh08ePCAKVOmsHDhQtq0aUPNmjVxcHAgJSWFGzduEBAQQGxsrDnZ0q9fv1xbKQshSrfY2Fj8/f25fv06kFF8eurUqTg6OhZzZEIIIYQoTVatWmVOXLz33nvUqFEjz2M9PT1zrF9y/fp1NmzYwPHjx7l16xZarRZPT09at26Nr69vttn0kZGRdOvWLcu2gICALN162rRpY14us3jxYpYsWQJkXaYzZcoUtm/fbv59+/btWX5/+DyA+Rrjx4/PtnRo27ZtTJ06FYCDBw9StWpVNm/ezI4dO7hy5QoGg4EnnniCF198EV9fX6sJoOHDhxMQEJDt2tbs3r2bLVu2EB4eTmpqKjVr1qR37968/vrrlCtXLtfxQpQ2NkmmAMydOxcHBwc2bdoEZHx52rt3r8VjTYmUYcOGMX36dFuFIIQogaKiopg5c6Z5+uszzzzDe++9J3duhBBCCJEvphnukFFzccCAATY79//+9z8WLVqEVqvNsj0yMpLIyEh27NjBm2++yTvvvGOzaz4KKSkp+Pn5ERAQkGV7SEgIISEh/PnnnyxfvrxQS5OMRiOTJ09mx44dWbaHh4cTHh7O7t27WbNmTZ4L8QpRWtgsmaJUKpk5cybPP/88K1eu5I8//sj2ZgQZU986d+7MyJEjadmypa0uL4Qooe7cuWPu2NW7d2/GjBkja4mFEEIIkW9hYWHEx8cD0KpVKxwcHGxy3hUrVrBw4UIAGjZsyEsvvUTt2rUpX748V65cYePGjQQFBfHNN9/g7u7Oa6+9BkDlypX5+eefAZg6dSrBwcH4+Pgwb94887nzEuPEiRN5/fXXeeONN7hz5w7dunXj3XffzXJMQZ/rxx9/zLlz5xgyZAi9evWiQoUKXL9+nWXLlhEeHs7Ro0fZvHkzvr6+BTo/wMaNGwkODqZFixa89tpr1KxZk+joaDZv3szRo0eJiIjgP//5Dz/88IN8BhSPFZslU0xatWpFq1atSE9PJzQ0lDt37pCcnIyjoyOVKlWiQYMG2NnZ2fqyQogSqkWLFrzzzjvcunWLV155BYVCUdwhCSGEEKIUCg8PNz/OS8OLvLh8+bK5MO2ECRN46623snxW8fHxoW/fvkyePJldu3axaNEi+vXrh6urKxqNhieffBLAvHTZ0dHRvC2vKleuTOXKlc2zdl1cXPJ9DmuCgoJYtGgRffr0MW9r3LgxHTp0oE+fPsTExBQ6mRIcHEzXrl1ZsmSJOVni4+NDt27dmDFjBlu2bCE4OJgffvhBamWKx4rNkykmdnZ2NG3atKhOL4QowUwJVJNnn322+IIRQgghxGPh/v375scVKlSwepzBYODy5ctW92dOVKxcuRKtVkuzZs0YN26cxeOVSiUff/wxv/76K8nJyezbt4+hQ4cW4Bk8er169cqSSDFxcXFh4MCBfPvtt4SHh5OQkED58uULdA17e3s++eQTi7NOpk6dysGDB7l79y6bNm2SZIp4rBRZMkUIUTbt2bOHzZs3M2/ePLy8vIo7HCGEEOKxk1P745s3b1odd/PmTatJgNLQNjkpKcn8OKdlL4mJibzwwgtW92cu/nr48GEgI+mQE9NskeDgYIKCgkpNMiWn16FRo0bmx5GRkQWe7dOhQwer9VAcHBzo3bs369atIywsjHv37uWYCBOiNCmyZMqtW7eIiIjgwYMHaLVa+vfvX1SXsqnz589z9OhRAgMDuXz5MnFxcWg0GqpUqULr1q156aWXaNy4cZ7OpdVq2bJlC7t37+bq1aukpqZSpUoVOnbsyIgRI/JVfVyIks5oNLJ582ZzEer58+fz1VdfoVTapAO7EEIIIf6fqf1xfmm1Wqvj9u/fX+KTKU5OTubHKSkphT5fVFQU9+7dA2DBggUsWLAgT+Pu3r1b6Gs/Kk888YTVfa6urubHmRNV+eXj45Pn/eHh4bRt27bA1xKiJLF5MmXLli2sWrXK3ALV5OFkyrJlyzh16hSVK1fOUqSpOPn6+hIYGJhtu+kfnitXrvDDDz/g5+fH5MmTc6z9cPfuXUaPHs3FixezbL927RrXrl1j69atLFy4kO7du9v8eQjxqOn1er7//nv27NkDZNy9mTBhgiRShBBCCGEzbm5u5semJIglLi4uWWafQPb2w4C5QH5+paamFmhcccipJXHmz2l6vb7A1/Dw8MjzflMBYSEeBzZLpiQlJTF+/HhOnDgB/Nv+GLCYdGjevDlfffUVCoWC119/nfr169sqlAIztW6tUqUKzz33HK1ataJKlSqkp6dz6tQpVq1aRVxcHKtWrUKtVvP+++9bPI9Op2PcuHHmRMrzzz/PoEGDcHJyIjAwkGXLlpGYmMh7773Hxo0bc83mClGSabVaFi1axLFjxwCoVKkSs2bNkiU+QgghhLApb29v8+NLly4V+nwGg8H8eMKECfTo0SNP42zVRUgIUbrZLJkyadIkjh8/DkCNGjXo06cP8fHxbN682eLxbdu2pWLFisTGxnL48OESkUypW7cukyZNokePHtkKKLVs2ZLnn3+el19+mbt377Jq1SqGDh1KzZo1s51n27ZtnD17FgA/Pz+mTp1q3te8eXPatGnDsGHDSEtLY+7cuWzcuLFon5gQRSQ5OZl58+Zx7tw5AGrWrMmsWbNyvUMhhBBCCJFf3t7euLq6Eh8fT2BgICkpKYVKbGSe6ZK5M4/In9xm+GTen3lpkRClnU3m4P/+++8cOXIEhULBgAED2Lt3LxMnTqRDhw5WxygUCp555hmMRiNnzpyxRRiFtnz5cp577jmr/c9r1KjBW2+9BWTMPjl48KDF41auXAmAu7s77733Xrb9TZs2ZciQIQCcPn2a8+fP2yJ8IR6p+Ph4pk+fbk6kNGjQgHnz5kkiRQghhBBFQqFQ8OKLLwIZRWYfXraTXzVq1DB3sCkp30dKo+Dg4Dzvl4SVeJzYJJmyY8cOAGrXrs2cOXNQq/M24aVBgwYARERE2CKMR6JNmzbmxzdu3Mi2PyIigqtXrwLQu3dv7O3tLZ5nwIAB5sfWqrELUZLpdDrzutdWrVoxe/bsArfUE0IIIYTICz8/PzQaDQBffPFFjt2LcqNSqejcuTMAR48eNX+GLy6m7w3p6enFGkd+HTt2zGpR3pSUFH799VcgI5EinXzE48QmyZSzZ8+iUCjo37+/1VkdllSsWBEoXRWxtVqt+bGl55o5q926dWur52nUqBGOjo7ZxghRWnh4eDBr1ixeeOEFpk2bZjVxKIQQQgjb6tGjB3Xq1LH4Y0o0WKLRaKyOy2u9kOJWvXp18xL6hIQEhg0bZrGBRGZGo5EHDx5Y3DdmzBhUKhV6vZ4JEyYQHR1t9Tx6vZ5du3Zx+/btgj+BHJjaC1u6YVuSpaWlMXPmzCw1aEwWLFhATEwMAC+//PKjDk2IImWTmimmdXCW6ofkxPRmnzlBUdKdOnXK/LhOnTrZ9mduN2dpv4laraZmzZqEhoaWqpk5omy7f/8+7u7u5t+rV69e4tsoCiGEEI+b0aNHW/33d+jQoVbbH9eoUYMffvihKEN7JHx9fbl9+zbfffcdd+7cwdfXl7Zt29KlSxdzXRW9Xk9MTAwXL15k7969XL58Gcje3cbb25sPP/yQefPmER4eTt++fRk6dKi5vmNqaipRUVGcPXuWX3/9lZiYGH7++WeqVKnyf+zdeXhMZ/vA8e9ka4g1EUGorfZdia3RIg1K7GqnpVTxakst1ZZYY229lqpfFaX2JUhsCbGvpUiUiK1ENKtEZJNtfn/kmvNmmZlsE0Nyf66r13VmzjnP3HNmmvbc8zz3bfD31axZMy5duoSfnx//93//R/v27ZWaMJaWltjZ2Rn8NQ2hYcOGHDt2jCFDhjBixAiqVKlCaGgo27dv5+TJk0DaD8kDBgwwbqBCGJhBkilvvfUWSUlJJCcn5+o8TUuzN6UQUUJCAps2bQLSEkGdOnXKckz6THV2f/AqVKiAv78/kZGRJCYmYmFhofPYuLg4vWPFx8fr3S9Efv35558sXryYCRMmKFNihRBCCCGMYfLkydSrV48lS5bw9OlTLl68qHQV1cbMzAxnZ2et3Tg/+eQTihcvzoIFC4iOjmbdunWsW7dO6zjm5uYFNht38ODBbN++naioKJYtW8ayZcuUfQ4ODmzevLlAXje/Bg8ezMWLFzlw4IDWGffVq1fnl19+yXEpCCHeFAb5RpcvX56YmJhcz7DQdLypUqWKIcIocMuXL+fJkydA2h8NbcmS2NhYZVuzjEeX9NXHY2Nj9SZTmjVrlttwhTAYHx8fVqxYQWpqKitXrqRx48YZZqgIIYQQQrxqH330EU5OTnh5eXH27Fl8fX2JiIggJiaG4sWLU7ZsWerWrUvz5s3p1q2bsoxGm48//piOHTuyfft2zp49yz///MOLFy+wsLCgfPny1KlTh7Zt2+Ls7FxgdT/s7OzYtWsXa9eu5fLly4SEhPDy5csCeS1DUqlULFmyhHbt2rFr1y7u3btHfHw8b7/9Nl26dGHUqFHSTloUSiq1Wq3O7yCurq5s376dqlWrcvjwYUxM0kqxHDt2jAkTJqBSqbL0gg8PD8fJyYmXL18yZswYvv766/yGUaCOHDnCl19+CaQV2t23b5/WPwojRoxQsuK3b99WroU2U6dOZf/+/UBaRyR90wXr1KmT41ivXbuWbSJHiJxyd3dnw4YNAFhYWDBlyhRatWpl5KiEEEIIoY2+ZT41atQoFMt8hBDidWCQmSm9evVi+/btPH78mJ9++onJkyfrPT4hIYHJkyeTkJCAmZkZ/fr1M0QYBebatWtMmzYNgFKlSrFy5Uqd2dX00/6SkpL0TgNMX6k7u+mC165d07s/Pj6etm3b6j1GiNxQq9Vs3LhRaTtoZWXF999/T4MGDYwcmRBCCCGEEEIYl0GSKU2bNqVr164cPnyYdevW8fjxY0aOHJmlhkpISAhnz57l119/5dGjR6hUKgYOHPhaL/O5e/cun3/+OQkJCVhaWrJmzRq9/dGtrKyU7bi4OL1JkvR1TtKfp43MNBGvUkpKCqtWreL48eMAWFtbM2vWLKpXr27kyIQQQgghhBDC+AxWBWjBggU8ffqUGzdu4OXlhZeXF5C2hg7SKjinX1GkVqtp27Yt06dPN1QIBhcYGMjIkSN5/vw55ubmrFixghYtWug9J/1SnZCQEL11JTTFasuWLau3XooQr9LLly9ZsmQJly9fBqBSpUq4uroWSNV6IYQQQhjWhx9+iLe3t859QgghDMNgyZRixYqxefNmli5dyrZt2zK0O1apVBn6jpubmzN06FAmT5782lZ1DgsLY+TIkYSGhmJiYsLixYtz1MEkfTvkBw8eULduXa3HpaSkKD3ka9asaZighTCA9P9O1qxZk1mzZlGmTBkjRiSEEEKInNLXNlkIIYThGDSTYWFhwYwZMxg9ejSHDx/mypUrBAUFKRW17ezsaNmyJd26dXutf+WOiopi5MiRSrJjzpw5fPTRRzk6t3nz5sr2lStXdJ73999/K+2O058jhLGZmpoyZcoUtm7dyoABA2SJmRBCCCGEEEJkYpBuPoVJbGwsn376KTdu3ABg+vTpfPrpp7kao0uXLjx8+JCyZcty6tQprXVTZs+ezdatWwHYtWsXjRs3zlfccXFxSvtk6eYjcissLAxra2tMTU2NHYoQQgghhBBCvPZ09+3Nhbp161KvXj2+/fZbQwxnNImJiYwfP15JpIwfPz7XiRSAkSNHAhAZGcmPP/6YZb+fnx+7du0C4N133813IkWI/Lh37x6TJk1i9erVSG5VCCGEEEIIIbJnkGU+ZmZmpKSk0LJlS0MMZzSTJk3iwoULAHTo0IEuXboQEBCg8/hixYpp7UTUp08f9uzZw/Xr19m4cSPh4eH07dsXKysrrly5ws8//6y0TZ4xY0aBvR8hsnPjxg0WLFhAfHw8x44dw9HRUZnhJIQQQgghhBBCO4Ms8+nQoQPBwcEsW7Ysx7VFXkd16tTJ1fEODg5s3rxZ677w8HBGjx7NrVu3tO4vXrw4S5YswcnJKddxaiPLfERunT17lh9//JHk5GRUKhWjR4+me/fuxg5LCCGEEEIIIV57BpmZUrduXYKDg3n48KEhhisUypUrx44dO9ixYwcHDx7k4cOHJCQkUKFCBRwdHRkxYoTWWS1CvAqHDh1i7dq1qNVqzMzM+Prrr3F0dDR2WEIIIYQQQgjxRjDIzBQvLy8mTpxI1apVOXjw4Gvb7rgwk5kpIifUajU7duxQih9bWlry7bffytIeIYQQQgghhMgFgxSgdXZ2xsnJiUePHjF16lQSEhIMMawQwsB+/fVXJZFSsmRJ5s+fL4kUIYQQQgghhMglg0whefr0KZMnTyYxMZHDhw9z7do1+vbty7vvvoudnR2WlpbZjlGpUiVDhCKE0KNChQoA2NraMnv2bCpXrmzkiIQQQgghhBDizWOQZT5169ZFpVIpj9VqdYbH2QahUuks1CpyRpb5iJzy9PSkTZs22NjYGDsUIYQQQgghhHgjGay4SeacjAFyNEKIfHr+/DnJyckZEifSsUcIIYQQQggh8scgyZTevXsbYhghhAGFhoYya9YszMzMcHNzo0SJEsYOSQghhBBCCCEKBYMkU9zc3AwxjBDCQB4/fsysWbOIiIgA4PDhw/Tv39/IUQkhhBBCCCFE4SA9jIUoZG7fvs3cuXOJiYkBoGfPnvTt29fIUQkhhBBCCCFE4WGQZMrLly+Jjo6mRIkSFCtWzBBDCiHy4MqVKyxcuJDExEQARowYQZ8+fXJVEFoIIYQQQgghhH4meT0xOjqaZcuW4ezsTNOmTWnfvj3NmzfHycmJpUuXEhkZacg4hRDZOHHiBPPmzSMxMRETExP+85//0LdvX0mkCCGEEEVUbGwsHh4exMbGGjuU117Hjh2pU6cO06dPN3Yob4xhw4ZRp04dhg0bZuxQRAGRz1i/PM1M+eeffxg5ciT//vsvkLFzT1BQEL/99huenp789ttv1KxZ0zCRCiF0OnToEL/88gsAFhYWTJkyhVatWhk5KiGEEEIYS0xMDBMnTsTX1xd3d3dWrFhRKIvRR0VFceDAAU6dOsWDBw+IiIjAwsKCcuXK0axZM5ycnOjYsaP8uCR0qlOnTobHu3btonHjxtme99FHH3H//n3l8eLFi+nZs6fB4xOvr1zPTElOTmbixIk8ffoU0N4SWa1WExwczFdffUVSUpJhIhVC6FSrVi0sLS2xsrLC1dVVEilCCCFEEZY+kQLg6+vLxIkTlXpqhcXWrVtxdnZm/vz5nD17lqdPn/Ly5UtevHjBw4cP2bt3L+PGjaN///74+/sbO9wcWblyJXXq1Mlygy9enQMHDmR7zM2bNzMkUkTeXLp0Sfm+X7p0ydjh5FqukyleXl4EBASgUqkoU6YMc+fO5fTp09y8eZPTp08zZ84crK2tAbh37x5HjhwxeNBCiIxq1arFd999x4IFC2jYsKGxwxFCCCGEkWROpGgUtoTK/PnzmT17Ns+fP8fc3JzevXuzYsUKdu3axZYtW3B1daVp06YA+Pn5MWTIkDfyZk28Om+99RaQNuM7OTlZ77H79+/PcI4omvKUTAGwtLTkjz/+oH///pQvXx4zMzPKly/Pxx9/zObNm5VCtN7e3oaNWAjBy5cvuXv3bobnmjRpQvXq1Y0UkRBCCCGMTVciRaOwJFQ2bdrEpk2bAKhUqRJ79uxh4cKFdO7cmcaNG9OiRQsGDRrEjh07+OGHH1CpVMTExPCf//xHKVMgRGaOjo6Ym5sTERHBuXPndB6XkpLCoUOHgLRaO6LoynUy5datW6hUKlxcXHTWQ6lZsyYuLi6o1Wpu376d7yCFEP8TExPDrFmzmDFjBnfu3DF2OEIIIYR4DWSXSNF40xMqgYGBLF26FAArKys2btyod0nM0KFDmTRpEgDPnz/nhx9+eCVxijdPmTJlcHR0BPQv9Tl79izh4eGYm5vz0UcfvarwxGso1wVow8PDAWjWrJne45o1a8bOnTuJiIjIW2RCiCyePXvGrFmzePToEQDu7u5SdV4IIYQo4nKaSNHQJFTexKK0mzZt4uXLlwBMmDCBqlWrZnvOqFGj8PT05M6dO5w5cwZ/f3/q1q2r95zr16+zfv16rl+/TmRkJLa2tjg6OjJ27FgqVqyo87zAwEC8vLy4fPkyAQEBhIeHY2JiohTEHThwIC1atMhy3qVLlxg+fHiG57QliY4fP07lypUzPBceHs7GjRs5ffo0QUFBJCUlUb58eRwcHBg2bBj16tXT+14BXrx4wbZt2zh16hQPHz7k+fPnFCtWjGrVqtGyZUt69uyZ7TXL7Pnz53h7e3PhwgVu3bpFcHAwSUlJlClThgYNGtCtWze6deuGqalplnP/+ecfevfuTVxcHE2aNGHr1q2YmWW9dY2NjaVnz54EBgZSrlw5PDw8lJITedGjRw98fHw4fvw4sbGxWFlZZTlGk2hp3749pUuXzvHYFy9exN3dnStXrhAeHo6pqSn29va89957fPLJJ9jZ2ek8Nzg4mE2bNnHu3DkCAwNJTEykTJkyWFtbU79+fRwdHfnwww+xsLAA4Msvv+TIkSOUKVOGM2fOKM9rEx0dzXvvvcfLly8ZOHAgs2fPzvF7ys9nnPn7nfn7D+Dm5kafPn2yPJ+fa2lIuU6mxMXFoVKq4zqjAAAgAElEQVSpKFWqlN7jSpYsCUB8fHzeIhNCZPD06VNmzpxJaGgoAG3atFF+aRFCCCFE0ZTbRIrGm5hQUavVyo2spaUlH3/8cY7OMzU1ZciQIcycOROAvXv3MmPGDJ3H79ixg9mzZ5OSkqI8FxQUxPbt2/H09GTt2rVaEyKBgYE4OTlpHfPJkyc8efIEDw8PRo4cybRp03IUe3ZOnTrF119/naX9dWBgIIGBgbi7uzNhwgTGjx+vc4zTp08zZcoUoqKiMjz/4sUL/Pz88PPz4+jRo/j4+OQqtt69exMUFJTl+bCwME6ePMnJkyfZs2cPq1evzvIdrFatGtOmTWPWrFncuHGDX375hQkTJmQZa968eQQGBirb+UmkQNqynZIlS/LixQu8vb3p1atXhv2xsbEcP34cIMede16+fMm3337LwYMHs+wLCAggICCA7du3s2zZMq3Lhi5fvszYsWOzfMZhYWGEhYVx584d3N3d8fDwoHbt2gD069ePI0eOEBUVhY+PD126dNEZ38GDB5UEZd++fXP0njTy8xnnRX6vpaHlqTWyEOLVun//Pq6urjx//hyAzp07M3bsWK1ZXiGEEEIUDXlNpGi8aQmVgIAA5Ya/RYsWuYq5Y8eOSjLl6tWrOo+7ffs2np6e2NraMnbsWBo0aEBcXBze3t5s27aNmJgYPv/8czw9PbPMUElNTcXc3BxHR0fatWvHO++8Q6lSpYiMjOTu3bts3ryZJ0+esH79emrUqEH//v2Vcxs1aoSHhwdbt25l27ZtAHh4eGSJL/0v7jdv3mT8+PEkJSVhYWHBiBEjeP/997GwsODGjRusXbuW8PBwVqxYQenSpRk6dGiW8S5cuMDYsWNJSUlRCvl27NiR8uXLEx8fT0BAAD4+Pvzzzz85vtYaKSkpNGvWjA8++IB69ephY2NDQkICgYGB7Nq1i6tXr3Lx4kVmz57NkiVLspw/cOBATp48yYkTJ1izZg3t27fP0LLY29ubvXv3AjBgwAA6dOiQ6xgze+utt+jcuTO7d+/mwIEDWZIpXl5exMfHU6pUKTp06MC1a9f0jqdWq5k4cSInT54EoFOnTnTp0oXKlStjYmLCjRs32LhxI0+fPmXixIls27aNRo0aKecnJiYyadIkZZbM4MGDadWqFdbW1iQlJfHo0SOuXLmi1DXVaNeuHRUrVuTff//F3d1dbzLF3d0dSGtokZOW0Onl5zP28PDAz89PSWwuWLAgw3sHqFChgsGuZUGQZIoQrzlfX1/mz5+vzPIaMGAAgwcPRqVSGTkyIYQQQhhLfhMpGm9SQiV9rbgGDRrk6lxbW1tsbW2VX/J18ff3p3LlyuzcuRMbGxvl+datW9O8eXMmTZpETEwMixcv5qeffsryGj4+PpQvXz7LuO3atWPw4MF88cUXnD17lp9//pk+ffooP4wVL16c2rVrZ3hNzSwDXWbOnElSUhLm5ub89ttvODg4KPuaNGmCs7MzH3/8MSEhISxZsoSPPvoow8yNhIQEpkyZQkpKClZWVqxbt47mzZtneI0WLVowePBggoOD9caizaZNm7Quw2rRogW9e/dm1apVrFy5Eg8PD8aNG6e1kcL8+fNxcXEhIiKCKVOm4O7uTvHixQkNDVXq31SrVs2gy9579uzJ7t27uXjxIqGhoRk+T83MqC5duuhdOqOxa9cuTp48iYWFBb/88gvt2rXLsL9p06b06tWLIUOGcPfuXRYsWKAk0yAt8RcWFgbAsmXLsiSMmjZtSs+ePfnuu+8yPG9iYkKfPn1YvXo1Z8+eJSwsDFtb2yzx3b9/nxs3bgBoXU6Tnfx8xrVr1yYyMlJ5XLlyZb3f+fxey4KQ6wK0GnIjJ0TBu3HjBq6ursTHx6NSqRgzZgxDhgyRf/+EEEKIIsxQiRSNN6Uobfobr3LlyuX6fE2iIikpSe97/fbbbzMkNTS6deum3Mx6e3tnqQ1ZvHhxrYkUDQsLC6ZOnQqkLd++detWrt+Dxo0bN/j777+BtBkc6RMpGhUqVFCSDAkJCezZsyfD/n379ik36t98802WRErmsXIru3o2X3zxBdbW1qjVap1LiGxsbJg3bx6QVkdl4cKFAMyYMYPIyEjMzMxYsmQJxYsXz3V8urRs2ZJKlSqRkpKSYTlJSEgIFy9eBNJqq2RHrVbz66+/AjBixIgsN/8apUuXZsqUKQD89ddfGWYBaeqVauLSxdLSEktLywzP9enTB5VKRXJystLKOTPNrBQzM7McvafMDPEZ54QhrmVByPPMFH3r7tJTq9XZFj1SqVT5+mMiRGFVs2ZN7O3tCQoK4quvvqJ9+/bGDkkIIYQQRmToRIrGmzBDJX3NiGLFiuX6/PQ33LGxsVrfZ5kyZfQuF+nTpw8nTpwgKSmJy5cv07VrV53HJiYmEhERQVxcnFJ/JTU1Vdnv7++f52UI58+fV7b79eun8zhnZ2dKly7N8+fPuXjxIqNHj1b2aZZLlChRIte1MnJLrVYTFhZGTEwMycnJyvN2dnY8e/YMf39/ned27NiRAQMGsGPHDnbs2EFMTAxnzpwB0m7Wc7s0JTuazrVr167lwIEDfPrpp0DaspTU1FTs7e211szJ7N69ezx+/BhIW6KvT/pEyfXr16lWrRpAhtkk+/bt07pUS5fKlSvTtm1bzp07x759+/jss88y7E9JSVGSLO3bt89TgjK9/HzG2THEtSwI+Vrmo1arde5TqVTKr+f6jhNC6FaiRAlcXV158uQJTZo0MXY4QgghhDCigkqkaLzuCZX0nVXy0uQiLi5O61jp1atXT29NuvTJj7t372ZJpiQmJrJt2zYOHDjAnTt3SEpK0jlW5oKvuXHv3j0gbUaCvqURZmZmNGjQgPPnzxMQEJBh3+3btwFo2LAhb731Vp5j0efYsWNs27aNv/76K8P1zyy7azF9+nQuXrzIo0ePlNkiTZo0YezYsQaNV6NHjx6sXbuWW7ducf/+fWrWrKks8XFxccnRLPGbN28q2/oSXplpZgsBvPvuu1SpUoXAwEDmzp2Lh4cHTk5OtGzZkgYNGmBubq53rH79+nHu3Dnu3r2Lr69vhsTTuXPnlMYW+UmmGeoz1scQ17Ig5GmZj1qtzjZBojlGEilC5FxqaiqXL1/O8JyNjY0kUoQQQogirqATKRqv85KfsmXLKtvplz/klGZZjrm5uc5kkbblPbr2axoDaERGRjJgwAAWLFjAzZs39SZSAKWDSl5obkzLlCmDiYn+WzrNjANt8QJaa2nkV2pqKt9++y3jx4/n7Nmzem+yIW0Zkj7FixfP0IHJ1NSUxYsXa22XbAjvvPOOUpdn//79+Pv7K7V2crocJvMysJxKfy3Mzc355ZdfqFGjBpA202Lp0qUMGDAABwcHxo4dy7Fjx3Teczs5OVGmTBkApVivhmaJj7W1Ne+//36u4zT0Z6yPIa5lQcj1ty8/03OEELolJSWxfPlyzpw5w8iRI7NUDxdCCCFE0RQbG/tKEikamoTKypUrdc7gMIY6deoo27ktEaBpI5t5HEOaP3++Elfnzp3p06ePUlTWwsIClUpFamqqUgLBED86v6519Pbs2aPcvNevX58RI0bQpEkTypcvT7FixZQE0JAhQ7hy5UqOxvzjjz+U7ZSUFC5evFigSzh69OjB33//jaenp5IYa9CgATVr1szR+emXdP366685rjuTOaH3zjvv4OHhgY+PDz4+Ply+fJmgoCDi4uI4ceIEJ06coG3btqxevTpL7RgLCwtcXFzYvHkzhw4dYsaMGVhYWBAdHa20eO7Ro0e2M1y0KYjPWBdDXUtDk24+QrwG4uPjWbhwodJe7dixY3z00Uc5qhIuhBBCiMLNx8fnlSVSNHx9fTlx4gTdu3d/pa+rT+3atZX6H1euXCEmJibHy5FOnDihbOurd5HdL+Dp95cuXVrZjomJ4ciRI0Dazam2Vr+QdXZIXmlmG0RGRpKamqp3dopmFk/6eCFtpk9wcHCBLIXYtWsXkFagdPv27TqXEUVHR+dovC1btih1UkqUKEFMTAyLFi2idevWBZZQ6datG4sXLyYoKIgtW7YAOZ+VAv/7jABKlSqVbXcmfczMzHB2dsbZ2RmAf//9lxMnTvDHH39w//59zp8/z48//sj333+f5dz+/fuzefNmnj9/rtxjHDx4UJkZldclPob+jPUx5LU0pDx38xFCGEZ0dDQ//PCDkkipW7cubm5ukkgRQgghBJBWgNPQRTaz07hxY72FWI1BpVIpN7Px8fHs3r07R+elpqaydetW5XHv3r11Hnv79m2lWKw2fn5+ynatWrWU7X/++UeZvaCvKG362g/a5HSmyTvvvAOkLWPIXAslveTkZGW2TOYbUM0MGT8/v3wtOdLm7t27QNp3V9dNdmxsLA8fPsx2rAcPHrB48WIAmjVrxrZt27C0tCQuLo6pU6fq/bzyw9bWljZt2gBpS7JMTU1zlVxM34Tlr7/+MmhsFStWZPDgwezatQt7e3sAjh49qvXYOnXqKLV+NDNJNEt8GjZsmOfEhCE+45x+3wvyWuaHJFOEMKKwsDCmTZum/Efw3XffZc6cOZQsWdLIkQkhhBDidWFlZcWKFSteWUKlcePGrFix4rVa4qMxYsQI5QenlStXEhgYmO05GzZsUIqtOjo6UrduXZ3HRkVFKV1utNHcjJqZmWXoGpK+e4m+Og07duzQG2v6H9MSExN1Hte2bdssMWnj7e2t1Fdp3bp1hn0dO3YE0m54M7dNzi/N9dBXKHjPnj3Z1pVJTk5mypQpJCQkULx4cZYsWULt2rX55ptvgLQW0WvWrDFc4Jn06tULCwsLLCwsct3xpkGDBspylO3bt+v9PPPKyspK+buQvnV4ZpqirefPn+fChQvcuHEDSOtOlVeG+IzTJ2H0XZ9XcS3zQpIpQhjJ48ePmTp1KkFBQQB88MEHfPfdd1l6xAshhBBClChR4pUkVDSJlNexmw9AlSpVmDx5MpC2tGbEiBHKL+TabN26laVLlwJpywPmzp2b7WssXLiQZ8+eZXn+8OHDynIhJyenDDfWb7/9tvIr+759+7SOu3PnTry9vfW+dvny5ZVtTStYbZo0aaIUSN26davWmhQhISEsXLgQSOv6k3k5R48ePZTis0uXLlVmSWsTHBysN+7MNEtvTpw4oXVp099//83y5cuzHWfVqlXKbJ4ZM2ZQpUoVAIYOHcp7770HwJo1azLMGDIkFxcX/Pz88PPz45dffsnVuSYmJnz++ecAPHr0iOnTp+tNAsTExGSoCwNw5coVvd+DmJgYZQlg5cqVdR7XvXt3ihUrRkpKClOmTAHSEnf5WcZniM84ffFjfe/TENeyIEjNFCGMIDQ0lOnTpyuV8nv06MHIkSOzrcYuhBBCiKJLk1ApqGK0r3siRWPEiBE8fvyYLVu2EBQURO/evXFxcaFDhw5UrFiRly9fcu/ePfbt26ckCEqUKMGqVauoWLGi3rHr1q3LvXv36Nu3L59//jkNGjQgPj4eLy8vZamQlZUVU6dOzXCetbU1jo6OnD59mlOnTjFq1CgGDRpEhQoVCAsLw8PDg4MHD9KsWTO9SYtmzZop225ubowdOxZbW1slUWNvb690sJkzZw4DBw4kKSmJkSNH8sknn9C+fXssLCy4ceMGa9euVeqhTJkyBWtr6wyvZWlpyZIlSxg1ahSxsbEMGzaMPn360LFjR8qXL098fDz37t3Dx8eH+/fvc+zYsZx8PEDa/9suXbqUkJAQBg4cyOjRo3nnnXeIj4/n9OnTbNmyhbfeeotq1arxzz//aB3j2rVr/N///R8AnTp1on///so+lUrFggUL6NGjB1FRUUyZMoV9+/a9dj9KDho0iPPnz+Pt7c3Bgwe5efMmAwYMoHHjxkrtlwcPHnD58mV8fHywsLBg6NChyvkXLlzg559/xsHBgffff586depQtmxZ4uLiuHfvnvLvAMDAgQN1xlGiRAm6dOmCu7u78p1wcnLKUkcnNwzxGVeqVIkKFSoQHBzM+vXrqVChAtWrV1fak9vY2Ch/j/J7LQuCSi29iwuFuLg45Y/vtWvXslRyFq8XtVrN6tWr8fLyYvjw4fTt2/e1rcYuhBBCiNdLQbRJflMSKen98ccf/Pe//822wGXDhg2ZP3++3uU9HTt2VBIzTZs2Zc6cOVprcVhZWbFmzRpatWqVZV9QUBCDBw/WOYvjnXfeYf369bRv3x6ACRMm8J///CfLcV999RWHDx/WOsbx48czzEA4deoUX3/9NbGxsVqPNzExYfz48UyYMEHrfoCTJ08yZcoUvdfR3t4eHx+fDM8NGzaMy5cv4+DgwObNmzPsS0xMZMyYMVy4cEHreKVKlWLlypWsXr1a6xixsbH06tWLx48fY2Njg6enZ5ZkEMCRI0f48ssvgbSuMTNnztT5HnTRdHfq168f8+fPz9W5ly5dYvjw4QAsXryYnj17ZjkmKSmJ+fPns3379mw7OFWuXFnpsgNpS9lWrVqVbRwDBgzA1dVV7w+zV65cYciQIcrjdevW4ejoqHfcgvyMNbZu3crs2bO1juHm5pZhKVJ+rmVBkJkpQhiBSqXiiy++oF27dhl+gRBCCCGEyI6hZ6i8iYkUSFvq0a1bNw4cOMCpU6e4f/8+z549w8LCgnLlytGsWTOcnJzo1KlTrn60GjhwILVq1WLjxo1cu3aNqKgobG1tcXR0ZOzYsVSqVEnrefb29ri7u7N27Vp8fHz4999/KV68OJUrV6ZLly4MGzaMYsWKZfv6S5YsoWHDhhw9epSHDx8SExOj88bx/fff5+jRo/z++++cOnWKoKAgkpOTsbW1pVWrVgwdOpT69evrfb0PPvgAb29vtmzZwqlTp3j06BEvXrygRIkSVKtWjVatWtGrV6/sL1w6FhYW/Prrr2zZsoX9+/fz4MEDTExMsLOzo3379owYMQJ7e3tWr16t9Xw3Nzdl2ce8efO0JlIAunTpQq9evdi3bx9bt26lQ4cO2SYIXjVzc3NcXV0ZOHAgu3bt4vLly/z777/ExcUp348GDRrQvn37LEWfR44cSe3atTl//jy3b98mNDSUiIgI5Vo2bdqUfv364eDgkG0cLVq0oFKlSjx9+pQKFSrQrl27fL2v/H7GGoMHD6ZcuXJs376d27dvEx0dnaEGUXr5uZYFQWamFBIyM+X1d+rUKVq3bq2z2rUQQgghRG4YYobKm5pIEULkzrNnz2jfvj1JSUmMHTuWr7/+2tghvfGkQIMQBUytVvP777+zbNkyFi1apDPTKoQQQgiRG/ktSiuJFCGKjv3795OUlIRKpcpXFx/xP5JMEaIApaSksHLlSqXd3L179wgPDzdyVEIIIYQoLPKaUJFEihBFR2JiolKvpE2bNlStWtXIERUOkkwRooC8fPmShQsXKpXPK1SowOLFi5Ue6UIIIYQQhpDbhIokUoQo/KKionj06BHXr1/n66+/Vrr+jBkzxsiRFR5SgFaIAhATE8O8efO4desWANWrV8fV1ZWyZcsaOTIhhBBCFEY5LUoriRQhiobNmzdn6QTUrVs32rRpY6SICh+ZmSKEgT179owZM2YoiZRGjRqxYMECSaQIIYQQokBlN0NFEilCFD3m5uZUq1aNiRMnsnDhQmOHU6hIN59CQrr5vB6Sk5OZOHEiT548AaB169Z88803WFhYGDkyIYQQQhQV2rr8SCJFCCEMS2amCGFAZmZmDBo0CJVKhbOzM9OmTZNEihBCCCFeqcwzVCSRIoQQhiczUwoJmZnyevH396dOnTqoVCpjhyKEEEKIIio2NpYTJ07QoUMHrKysjB2OEEIUKjIzRYh8unjxIoGBgRmeq1u3riRShBBCCGFUVlZWdO/eXRIpQghRAKSbjxD5cPToUdasWYO1tTWLFi3C1tbW2CEJIYQQQgghhChgMjNFiDxQq9Xs3LmT1atXk5qayosXL/j333+NHZYQQgghhBBCiFdAZqYIkUupqan89ttveHh4AFCyZElmzpxJnTp1jByZEEIIIYQQQohXQZIpQuRCUlIS//3vfzl9+jQA5cqVY/bs2VSpUsXIkQkhhBBCCCGEeFUkmSJEDiUkJODm5sa1a9cAqFKlCq6urlInRQghhBBCCCGKGEmmCJFDK1euVBIptWvXZubMmZQqVcrIUQkhhBBCCCGEeNWkAK0QOTRkyBBKly5N8+bNmTdvniRShBBCCCGEEKKIkpkpQuRQpUqVWLRoEeXLl8fMTP7VEUIIIYQQQoiiSmamCKGDv78/p06dyvBcpUqVJJEihBBCCCGEEEWc3BUKocXVq1dZuHAhSUlJWFlZ0aJFC2OHJIQQQgghhBDiNSHJFCEyOXXqFMuXLyclJQUTExMiIyONHZIQQgghhBBCiNeIJFOESOfAgQOsW7cOAHNzc7755hvatGlj5KiEEEIIIYTGpUuXGD58uNZ9lpaWWFtbU69ePbp27UrXrl2zLNFeuXIlq1atAmDTpk20atUq29esU6cOAA4ODmzevDlHcfbq1Yvbt28D0KdPH9zc3HJ0Xk5o4skpe3t7fHx8tO5Tq9UcPXqUffv2cfPmTaKioihVqhTvvPMOPXv2pHfv3piY6K4Ocf/+fc6fP4+vry93794lIiKCqKgozMzMsLW1pUmTJvTu3Zu2bdvmKNaTJ0/i7u6On58f4eHhAJQrV46GDRvSu3dvOnTokO0YYWFhbNu2jbNnz/Lw4UPi4uIoXrw41apVw9HRkUGDBmFra6v13PTfj5xyc3OjT58+uTpHvPkkmSIEaf8R+eOPP9i1axcAxYsX57vvvqNRo0ZGjkwIIYQQQuRUQkICT58+5enTpxw/fpzff/+dNWvW6LxxLigBAQFKIgXAy8uLWbNmYWlp+Urj0KhevbrW56Ojo/nqq684d+5chucjIiKIiIjg0qVL7N27lzVr1ujsZLlmzRo8PDyyPJ+YmMijR4949OgRBw4cwNnZmSVLlui8Bi9fvmTSpEkcO3Ysy76goCCCgoI4evQoTk5O/PTTT1hYWGgd5/Tp00yePJno6Ogs79XX1xdfX182b97Mjz/+iKOjo9YxckvX9RWFmyRTRJGXkpLCmjVr8PLyAqBMmTK4urpSo0YNI0cmhBBCCCH0GTRoEIMHD1Yex8XFcfPmTdavX09QUBB+fn6MGzeOnTt3olKpXllc+/fvB9J+oIuLiyMmJobjx4/TrVs3g4yvLXmR2YYNG9i7dy8APXr0yLI/NTWVL7/8kvPnzwPQuHFjPvnkE6pVq0Z0dDReXl5s376dK1euMHHiRDZs2KD1GpqZmdGsWTOaNWtG7dq1sbW1pWzZsjx79oyAgAC2bdtGYGAgXl5eqFQqVqxYoTXeuXPnKokUGxsbPvvsM+rXr4+ZmRkBAQGsW7eOoKAgjh07xty5c5k7d26WMR4/fsyECRN4+fIlAJ06daJnz55UrFiR0NBQjh8/jru7O9HR0UyYMAEPDw/efvvtDGMMHjyYzp076722cXFxDBkyhOTkZKpWrUqzZs30Hi8KJ0mmiCLvwIEDSiKlQoUKzJ49m4oVKxo5KiGEEEKI/ElNTeX69es0bdpU7zKNN5mNjQ21a9fO8FzTpk1xcXGhf//+PHr0CF9fX06cOEHHjh1fSUypqalKssPFxYVbt27h5+fH/v37DZZMyfyeM1Or1Vy+fBlIS+g4OztnOebIkSNKIsXR0ZE1a9Zgbm6u7G/Tpg1NmjRh2rRpXLhwAU9PT1xcXLKMM3/+fExNTbXG4ejoyNChQxkxYgTXrl3j6NGj+Pv7U7du3QzHhYeHs2fPHgBKly7N3r17qVChgrK/RYsW9OjRgx49ehAUFMTu3bv56quvsLGxyTDOhg0blETKqFGjmDp1aob9Tk5O1K9fn3nz5pGQkMDGjRuZOXNmhmNsbGyyjJuZu7s7ycnJgPZElSgaCudfVSFyoVu3bjRs2JDq1auzcOFCSaQIIYQQolDYuXMnY8aMYefOncYO5ZUrXbo0Y8aMUR6fOXPmlb32xYsXCQkJAdKSKZoExLlz54iIiHglMVy9epUnT54A4OzsTLFixbIcs2/fPmV75syZGRIpGr169eLdd98FUOoKZqYrkaLx1ltvZahxc+XKlSzH3Lhxg9TUVCCtvkz6RIpGiRIl+OSTT4C0hNWNGzeyHHP9+nUAVCoV48aN0xrP0KFDKVOmTIbjc0sz8wigZ8+eeRpDvPkkmSKKPAsLC7777jvmz5+PtbW1scMRQgghhMi3hIQE1q9fD6T9Wp+QkGDkiF69xo0bK9tPnz59Za+rudGuVKkSLVq0oFu3bpiampKcnIynp+crieHAgQPKtq6b/b///huAatWqZVnqkp6mroi/vz+PHz/OUzzFixdXthMTE7PsT0pKUrarVKmic5z0caY/J/NzZcqUoUSJElrHUKlUVK5cWecY2QkJCeHSpUsANG/eXG+8onCTZIoocoKDg9m0aZOS/QawsrLS+QdXCCGEEOJN4+7uzrNnz4C0YqLpZyEUFem7+KSkpLyS14yLi1OWj3fv3h2VSkW5cuWU7pDpZzQUlMTERA4fPgyAnZ0drVu31npcVFQUQLY/JpYrV07Zvnr1ap5i0sQD2ou1VqtWTdkODAzUOU76ZE7VqlV1jhMVFUVMTIzOcTSzdrSNkZ0DBw4o9xG9evXK9fmi8JBkiihSHjx4wNSpU9m9eze///67scMRQgghhDC4hISEDMVCVSpVkZydEhAQoGyXL1/+lbymt7c3cXFxABnqi2i2//77b+7du1egMZw4cULpZOPi4qKzXo5mtoi+pAOQoStOTmNXq9VKN6CJEycqyTxNa+LM6tatqxRxdXd3V5ZJpRcTE6P8/3uTJk2y1F0BGDBggPL6v/zyi9bYtmzZoiSS+vfvn6P3k55m1o+FhQVdu3bN9fmi8JACtKLIuHnzJvPmzVP+A2dqaoparZcjTtIAACAASURBVH6lld2FEEIIIQpa+lkp8L8b23379jFw4EAjRvbqJCcns2HDBuWxg4OD1uOePHlC2bJlDfa6mhvtOnXqZCgS++GHH+Lq6kp8fDz79+9n8uTJBnvNzNLPftE3c6JGjRpcv36d+/fv8+zZM50zVNLXOAkODtb72n369FGWD2Vmb2/PqlWrMswYSm/+/PmMHj2aoKAg+vTpo3TzMTU15e7du6xbt44nT55gZ2fHggULtI7h6OjIZ599xrp16/j111959OgRLi4uVKxYkbCwMI4dO4a7uzsAn3zyCe+//77e95PZ7du3lSRdhw4ddLaLFkWDJFNEkXDx4kWWLFmirIscNWqUFIsSQgghRKGTflaKWq1WntfMTunVqxeWlpZGjLBgaVojr1y5Uikuam9vz0cffaT1+BkzZhjstUNDQ7lw4QJAlq43VlZWdOzYkYMHD+Lh4cGkSZMK5Ae9qKgoTp8+DUD9+vWpVauWzmM7duzI9evXSUlJYcWKFbi6umY55tq1a5w6dUp5HBsbm+uYTExMGDduHJ9++qneZfU1a9Zk9+7dbNmyhXXr1rFw4cIM+83NzRk9ejQjRozA1tZW5zhTpkyhdevWrFmzBi8vL2XZlUaDBg2YMGFCnro75aQWjSg6JJkiCj0vLy9+/vlnUlNTMTU15csvv+SDDz4wdlhCCCGEEAaXeVaKRmGdnbJq1SpWrVqlc7+NjQ2rV6/GwsKiwGPx8PAgJSUFlUpF9+7ds+x3cXHh4MGD/Pvvv1y6dElnLZP8OHTokPLjYXY3+4MGDWLz5s2EhYWxbds2Xr58yciRI6lWrRrR0dF4e3uzdOlSTExMlJoz2S0VW758OQkJCajVap4/f861a9fYunUra9eu5Z9//mH27Nl6EyrHjx/H09NT6+skJSVx6NAh7OzsGDZsmM4xgoOD2bt3L76+vlr337lzhz179lCrVq1cFY9NSUlRWl6XLVuW9u3b5/hcUThJMkUUWmq1mj179rBp0yYgrS3b9OnTlfZuQgghhBCFia5ZKRpFZXYKQOXKlencuTOjRo3CxsZG53GbNm2iVatW2Y5Xp06dbI/RLK9p2bIlFStWzLL/vffeo0yZMkRFRbF///4syZSHDx/q7C5ToUKFHC0p0cycMDMz05rQSa9UqVL8/PPPjBkzhsjISPbu3cvevXszHGNiYsKsWbOYNWsWkLErjzaZuwI5ODgwePBgRo0ahaenJwEBAWzbtk1rQmXevHls3rwZQPnsNNfd39+fdevW4e3tzbx58wgICGDu3LlZxrh79y6ffvopYWFhWFtb89VXX/HBBx9gbW3Ns2fPOHnyJD/++CPHjh3jr7/+YsOGDVprr2hz4cIFwsLCAOjWrZvWVtKiaJECtKLQun37tpJIKVmyJHPnzpVEihBCCCEKLc2sFG2JFMg4O6WwGDRoEB4eHnh4eODp6Ym3tzdXrlzh+PHjTJ06VW8ixZD8/f25c+cOkHWJj4a5ublSsNTLyyvL7ItRo0bh4uKi9Z9jx45lG8OjR4+4du0aAO3atcvQhUeXxo0bs3//fgYOHJihdoxKpaJly5Zs3rwZJycn5fnSpUtnO2ZmJUuWxM3NDUgrCqytMOzx48eVREq/fv1YsWIFTZo0wdLSEktLS5o2bcqqVavo3bs3ADt37tR6TaZNm0ZYWBjFihVjy5YtDBgwADs7O8zNzbGzs2PAgAFs3boVS0tLnj17xrRp03L8PtLXopElPgIkmSIKsfr16zNw4EDKlSuHm5tbjrPOQgghhBBvmswdfHQpbJ19bGxsqF27NrVr16ZWrVq8/fbblCxZ8pXHkf5G+4cffqBOnTpa/9m2bRuQ1pkmJwmS3MhrPQ87Oztmz57NhQsXOHPmDEePHuXKlSv88ccftGjRIkM74po1a+Yptpo1ayptizPXMAHYs2cPkPb9nDhxos5xvv76a2U78yya27dvK8Vve/ToQY0aNXTGokl4+fv7c/v27Wzjj4uLUz6v6tWr07hx42zPEYWfLPMRhdqgQYPo3r27VNoWQgghRKGmq1ZKZoW1dooxpa+lkRv79+/PsBTHx8cnX3FokiklSpSgU6dOuT5fpVJpbSF969YtZTs/SYSyZcvyzz//8PTp0yz7Hjx4AKQlx+zs7HSOYWdnR7ly5QgPD+fhw4daxwCoV6+e3lgaNGjArl27gLTlVdkd7+XlpXQElVkpQkOSKaLQiI6O5rfffmPUqFFK8kSlUkkiRQghhBCFWna1UjIrSrVTXoXz588rtTRGjRpFgwYN9B5/4sQJPDw8OHfuHOHh4TlajpOda9euKTNIOnfubNDP9ejRowDY2trSvHnzPI8TGhoKpHU2ykzTLjk5OTnbcTTHZG6xbGpqqmxrCuZmN0bm83TRJMtUKhU9evTI9nhRNEgyRRQKYWFhuLq6EhgYyNOnT5k7d678z4EQQgghioSczkrRkNkphqVZ4mNubs64ceP0dqsBqFGjhtL5x9PTk08++cRgMYBhZ05cvnyZy5cvA9C/f/8sCYyc8vPzIygoCEBru+bKlStz9+5doqKiuH//vs7lRJpjIK3ldeYxNK5evcrQoUN1xvPnn39qPU+b9C2vW7ZsmeV1RdElNVPEGy8wMJBp06YRGBgIpE3/y0mGWQghhBDiTZfTWimZFbbaKcYSGxur1NJo27ZttokUSFuComnJm77OSV4lJiZy+PBhIC3B4ODgkONzQ0JCdO578OABkyZNUsYdPXp0lmMePnzIxYsX9b5GaGgo06dPVx5rS/Z88MEHyrabm5vWrkaJiYnMnz9fedyhQ4cM++vXr68sU/Ly8uLSpUta4zl79ize3t5A2n1Ddkt8PD09lZkussRHpCczU8Qb7c6dO8yZM4cXL14A0L17dz777DNMTCRPKIQQQojCL7ezUjRkdopheHl5ER8fD4Czs3OOz/vwww9Zv349f//9N/fu3eOdd97JcwynT59WZmu4uLjkKrE2a9YsoqKicHZ2pkGDBpQsWZLw8HDOnj3Ljh07SEhIwMrKip9++klrW+TQ0FBGjBhB/fr16dSpEw0bNsTGxgYTExNCQ0O5fPkyu3fvJjo6Gkhrldy3b98s4/Tp04eNGzfy8OFDzpw5Q//+/Rk6dGiG1sibNm0iICAASJvdo+nso2FiYsKkSZOYPn06ycnJjBo1ikGDBtGhQwfKli3Ls2fPOHHiBNu3byc1NRWASZMmZXvfoJn1Y2lpSZcuXXJ8bUXhJ8kU8cb666+/cHNz4+XLlwAMHTqU/v375/qXGSGEEEKIN1Fua6VkJrVT8k9zo21mZparoq+dO3dm/fr1yhiTJ0/OdwyQ+5kTarWaa9euKS2VM6tSpQpLly6lSZMmese5detWhkK12nTv3p05c+ZoTV5YWFjw66+/Mm7cOAICArh9+zbfffed1nFq167NmjVrsLCwyLKvd+/ehIWF8d///pekpCQ2bdrEpk2bshxnZmbGl19+Sa9evfTGfOfOHfz9/QHo1KlTjmYeiaJDkinijXTq1CmWL19OSkoKJiYmjB07VjLFQgghhChS8jorRUNmp+RPSEiIspSkRYsWlC1bNsfnNmnSBDs7O0JCQvDw8ODrr7/O08zq6OhoTp48CUCjRo10tgPWZcyYMVStWpUrV64QEhLC8+fPKVOmDNWrV6dLly707dtXb6KtefPm/Pbbb5w7d46bN28SEhJCeHg4iYmJWFlZUbVqVZo2bUrPnj2zLcxbpUoV9uzZg6enJ15eXty6dYvIyEggrRNQ/fr1cXZ2pnv37loTKenfU6dOndi+fTt//vknT548IS4ujuLFi1O5cmVatmzJwIEDc9TmuaBq0YjCQaXOSxpbvHbi4uJo1qwZkFbNW9s0vMIiOjqaMWPGEBcXh5mZGd988w1t27Y1dlhCCCGEEK9MQkICPXr0IDIyMk+zUjRUKhXW1tbs379fZqcIIUQuSGEJ8cYpVaoU3377LaVKlcLV1VUSKUIIIYQocjSzUvL7u2j62SlCCCFyTmamFBJFaWaKRnx8PMWKFTN2GEIIIYQQr5RmVkp+lvhkZmNjI7NThBAiF2RminjtJSYm8uOPPyrFnzQkkSKEEEKIoiggIMCgiRSAiIgIpVOKEEKI7MnMlEKisM5MiYuLY/78+fj5+VGiRAkWLVpElSpVjB2WEEIIIYTRqNVq/vzzT6XdrCGUKlWKli1bSldEIYTIIenmI15bkZGRzJ49mwcPHgBQtWrVXFVJF0IIIYQojFQqFQ4ODsYOQwghijRJpojXUnBwMDNnziQ4OBiAVq1a8c033/DWW28ZOTIhhBBCCCGEEEWdJFMKmL+/P5s3b+bChQuEhYVRsmRJatWqRe/evenZs6dMpdTi4cOHuLq6Kn3lnZycGD9+PKampkaOTAghhBBCCCGEkGRKgdqyZQtubm4kJSUpz0VERBAREcHFixfx8PBg1apVUkg1nZs3bzJv3jzi4uIA6Nu3L8OHD5ekkxBCCCGEEEKI14Z08ykgPj4+zJ07l6SkJMqXL8+sWbPYtWsXv/zyC++99x4AZ8+eZdq0aUaO9PWhVqv5/ffflUTKqFGjGDFihCRShBBCCCGEEEK8VqSbTwFISkqiS5cuPHnyhFKlSrFv3z7s7e2V/ampqXz55Zd4eXkBsHHjRtq0aZOv1yws3XwiIyOZMWMGH3/8MR06dDB2OEIIIYQQQgghRBYyM6UAeHl58eTJEwDGjh2bIZECYGJiwvfff4+ZWdoqq/Xr17/yGF8XmXN5ZcuWZcWKFZJIEUIIIYQQQgjx2pJkSgE4duwYkNa2rlevXlqPsbOzo23btgBcuHCBmJiYVxbf6yI1NZX169ezd+/eDM+bm5sbKSIhhBBCCCGEECJ7kkwpAH/99RcA1atXx8bGRudxLVu2BNKWBfn5+b2S2F4XycnJLF++nP3797Nx40ZOnTpl7JCEEEIIIYQQQogckWSKgcXExBAcHAxAjRo19B6bfv+DBw8KNK7XSUJCAvPnz+fkyZMA2NvbU69ePeMGJYQQQgghhBBC5JC0RjawkJAQZbtChQp6j7Wzs1O2NQkYXTQdbnSJj4/PQXTG9+LFC+bOnYu/vz8AtWrVYtasWZQqVcrIkQkhhBBCCCGEEDkjyRQDi42NVbaLFSum99j0HXeyS5ZoOvW8ycLDw5k1axaBgYEANG3alG+//Tbb6ySEEEIIIYQQQrxOJJliYImJicp2doVULSwslO2EhIQCi+l18OTJE2bOnEl4eDgAjo6OfPXVV1JsVgghhBBCCCHEG0eSKQaWPkGSlJSk99j0iRdLS0u9x167dk3v/vj4eKU70OvI29tbSaR069aN0aNHY2IiJXuEEEIIIYQQQrx5JJliYFZWVsp2dnVM0i/tSb/kR5vs9r/uhg8fTkhICNWqVWPAgAGoVCpjhySEEEIIIYQQQuSJJFMMLDdFZXNTrPZNlJqaqsw+MTU1ZerUqTIbRQghhBBCCCHEG0+SKQZWokQJKlSoQHBwcLbtjtPvz66N8pvm4MGDXL16lRkzZmBmlvY1k0SKEEIIIYTIr0uXLjF8+HCt+ywtLbG2tqZevXp07dqVrl27Kv8vqrFy5UpWrVoFwKZNm2jVqlWeY4mIiMDX1xdfX1/8/Pzw8/MjKioKgAkTJvCf//wnx2PFxMTwxx9/cPToUQIDA0lJScHe3p5OnToxfPhwbGxs9MZx6tQpfH19uX37NmFhYURGRpKcnEzZsmWpV68enTt3xsXFRWoWCmEgkkwpAM2bN+fQoUM8fPiQiIgInX/4rly5AqQVqm3UqNGrDLHAqNVqtm3bxvbt2wFYu3Yt48ePN3JUQgghhBCiKEhISODp06c8ffqU48eP8/vvv7NmzRpsbW0L5PUMVbPw/v37fP7550rXS427d+9y9+5ddu3axapVq2jevLnW8318fPj++++17gsJCSEkJISTJ0+yfv16fv75Z95++22DxC1EUSbJlALg5OTEoUOHUKvV7Nu3j1GjRmU5JjQ0lHPnzgHQpk0bSpQo8arDNLiUlBTWrl3LkSNHAChdujRdunQxclRCCCGEEIWLWq3m8uXLvHjxwmBjlixZEgcHhzeurt2gQYMYPHiw8jguLo6bN2+yfv16goKC8PPzY9y4cezcubPA31ulSpWoUaMGZ8+ezdV5L168YOzYsQQGBqJSqRg8eDCdO3fGzMyMM2fOsG7dOiIiIvjiiy/Yu3cv9vb2WsepXr06Dg4O1K9fHzs7O8qVK0dcXByBgYHs3buXq1evcvfuXT755BM8PT3f+JqMQhibJFMKwIcffoi9vT1BQUGsXbuWLl26ZPijl5qayty5c0lOTgZg5MiRxgrVYJKSkli2bBnnz58HoHz58syZM4dKlSoZOTIhhBBCiMLFz8+vQGb+rl+/nsaNGxt83IJkY2ND7dq1MzzXtGlTXFxc6N+/P48ePcLX15cTJ07QsWNHg7/++PHjadSoEY0aNaJcuXI8efKETp065WqMdevW8fjxYwBmzJiRYQnTu+++S8OGDRk/fjxRUVEsX76cJUuWZBmjT58+9O/fX+v4rVq1ol+/fri5ubFx40aCgoLYtWsXI0aMyFWcQoiMpIhFAbCwsOD7779HpVLx/PlzBg4cyLZt2/D19eXkyZOMHj0aLy8vADp37kybNm2MHHH+xMXFMXv2bCWRUrVqVRYtWiSJFCGEEEKIAlC7dm2sra0NOqa2pMSbrHTp0owZM0Z5fObMmQJ5nYkTJ9KhQwfKlSuXp/MTExP5448/gLTPddiwYVmOcXJy4v333wfA09OT0NDQLMeYmppm+1qjR49Wtq9evZqneIUQ/yPJlALSsWNHfvjhB8zNzQkNDcXV1ZX+/fvz+eefK1P/3nvvPRYtWmTkSPMnKiqK7777Dl9fXwDq16+Pm5ub3gJZQgghhBAi7ywtLfn0008NOuann36KpaWlQcc0tvSzbJ4+fWrESHS7ePEiMTExAPTs2VPnUqRevXoBaTPcfXx88vRa6Zf1JCYm5mkMIcT/SDKlAA0ZMoTdu3fTt29f7O3tsbCwwNramtatW7No0SLWrVtHsWLFjB1mvgQEBChdiRwcHJg9e3ahqP8ihBBCCPE66927N9bW1vmuA6JSqbCxsVFu1guT9F18UlJSjBiJbn/99Zey3bJlS53Hpd+X/pzcOHz4sLJdvXr1PI0hhPgfqZlSwOrWrcuCBQuMHUaBcXBwYOzYsQQEBDBhwoQcTTEUQgghhBD5o5mdsmzZsnyNo1arC+WsFEj70U+jfPnyRoxEN82PkgA1atTQeZytrS0lS5bkxYsX3L9/P8fjv3jxgqCgIPbv38/mzZuBtE6iAwcOzHvQQghAkikiD5KTkzNk+rt27UqXLl3euOrvQgghhBBvst69e7NhwwYiIyNRq9W5Pl+lUmFtbV0oZ6UkJyezYcMG5bGDg4MRo9EtODgYSFuCU7JkSb3HVqhQgRcvXhASEqL3uEWLFrF+/Xqt+ywtLVm4cCFVq1bNW8BCCIUs8xG5cunSJcaPH09YWFiG5yWRIoQQQgjxamlmp+QlkQKFc1ZKXFwcly9f5tNPP+X69esA2Nvb89FHHxk5Mu1iY2MBctSmWFMeQHNObnXu3JlDhw7RtWvXPJ0vhMhIZqaIHDt27BirVq0iNTWV2bNn/z97dx4e09k+cPw7EYkEkUWIrRVLosSu9jXWoiVtKRq1tZamWjSKVl+lCNWiluL91b7vSqKWoPYoRWhrC0Fs2RPZ1/n9kWvOOyOzJJFIxP25rl49mfOcM8+ZZxIz97mf++Hnn3+WaT1CCCGEEIUor9kpxSUrZcmSJSxZssTgfgcHB5YuXYqFhcUL7FXOpaSkAFlTb0zRXIPmGEOGDx+Oh4cHkBVcunHjBtu3b+fgwYOEhYUxa9Ysatas+Zw9F0JIMEWYpFar2bVrF2vXrgWy/pAPGTJEAilCCCGEEIUsr7VTimNWiraqVavSvXt3RowYkatVJkNDQ4mNjdW7r1y5clSsWDG/ugiApaUlAGlpaSbbalbg0RxjiKOjI46OjsrPjRo1ol+/fnz//fds2rSJAQMGsGbNGurVq/ccPRdCSDBFGJWZmcmaNWvYs2cPAKVLl+bbb7+lbt26hdwzIYQQQggBuc9OKS5ZKQADBw5k0KBBQNZ1WVpaYmdnZ7L+iCELFixg9+7devd5eHgwZ86cPPdVn9KlSwNZGSSmJCUl6RyTG2ZmZkyZMoVjx47x+PFjpk+fzrZt23J9HiHE/0jNFGFQeno6P//8sxJIsbe3Z86cORJIEUIIIYQoQnJbO6U4ZaU4ODjg4uKCi4sLtWvX5rXXXstzIKUwODk5AVnBlLi4OKNtNcVq85odY2FhQfv27QEIDAw0WchWCGGcZKYIvVJSUpg7dy4XLlwAsgp3TZ8+vcguKyeEEEII8SrLaXZKccpKKQhz5szJ9+wTY7SXQ75z5w4NGzbU2y48PFwJtjxPvRM7Oztl+9GjR/k+bUmIV4lkpgi9YmJiCAoKAqBWrVrMmTNHAilCCCGEEEVUTrNTilNWSnHQpEkTZVtzE1Mf7X3ax+SWdjZKTlYQEkIYJsEUoVfFihWZNm0arVq1YubMmZQrV66wuySEEEIIIYzw8PDA3t4elUqld79KpcLBwUGyUoqQli1bUqZMGQD27NljMBimqeNiZmaGu7t7np4rKSmJEydOAFlFbF977bU8nUcIkUWCKULx7DJrNWvWZMqUKRK1FkIIIYR4CZjKTpGslKLHwsICT09PAG7evMn69euztfH39+f48eMA9O7dW2+2+Pbt28nIyDD4PKmpqXzzzTdERkYC0K1bN6ysrPLjEoR4ZUnNFAHArVu3mDlzJl5eXjRv3rywuyOEEEIIIfLAUO0UqZVSMC5cuMD9+/eVn6Ojo5Xta9eusWvXLuVna2trevToke0cH3/8Mfv37+f+/fvMnj2be/fu0b17d8zNzTlx4gS//vorALa2towbN05vP6ZOncqSJUvo0aMHDRs2pHLlylhaWhITE8PVq1fZsWMH9+7dA7KWTvb29s6X6xfiVSbBFMHly5fx8fEhKSmJH374gWXLlumsTS+EEEIIIV4OmuyUn376SedxyUopGDt27DC4lPKRI0c4cuSI8nOVKlX0BlPKli3L8uXLGTVqFCEhIWzYsIENGzbotHFwcGDJkiVUqVLFYF+ePHnCmjVrjPbXzc2NH3/8UVlFSAiRdxJMecWdOnWK+fPnk56ejkqlYvjw4RJIEUIIIYR4iT2bnSJZKUVfzZo12bNnDxs2bODgwYPcv3+fzMxMKleuTOfOnRkyZAgODg4Gj9+5cycnTpwgMDCQBw8eEBERQXx8PFZWVjg5OeHm5kb37t3p0KEDZmZS6UGI/KBS53RBelGkJSYm0rhxYwAuXbqUozon+/fvZ8WKFajVaszNzZkwYQJt27Yt6K4KIYQQQogCtnnzZp3sFG9vbwYMGFCIPRJCiOJFwpKvILVazaZNm1i+fDlqtRorKyumTZsmgRQhhBBCiGJCs7IPICv4CCFEAZBgyismIyODFStWsGXLFgDKlSvHrFmzaNiwYSH3TAghhBBC5JdSpUoxfPhwAKmVIoQQBUBqprxiMjMzCQkJAaBChQrMmDGDypUrF3KvhBBCCCFEfuvfvz8uLi40atSosLsihBDFjtRMKSZyUzMlMTGR5cuXmyxkJYQQQgghhBBCiOwkmFJMGAumxMfHY21tLZW7hRBCCCGEEEKIfCDfrou50NBQvL29WbVqFRI3E0IIIYQQQgghnp8EU4qxu3fvMmnSJB49esTevXu5cOFCYXdJCCGEEEIIIYR46UkB2mLq33//5fvvvychIQHIWh6vWbNmhdwrIYQQQgghhBDi5SfBlGLowoULLFq0iNTUVACGDh3Ku+++W8i9EkIIIYQQQgghigcpQFtMaBegdXV1BcDMzIyxY8fSuXPnwuyaEEIIIYQQQghRrEhmSjGhHRNLT0+nVKlSjB8/nqZNm5KYmFiIPRNCCCGEeLlYWVmhUqkKuxtCCCGKMMlMKSYiIyNp3bp1YXdDCCGEEOKld+nSJaytrQu7G0IIIYowWc1HCCGEEEIILUlJSYXdBSGEEEWcZKYUE5mZmURHRwNQqlSpIpuampSUpGTQnDlzBisrq0LukcgJGbeXl4zdy0vG7uUk4/by0h67ixcvUrp06ULukRBCiKJMaqYUE2ZmZjg4OBR2N3LFyspKUmhfQjJuLy8Zu5eXjN3LScbt5VVUb0oJIYQoOmSajxBCCCGEEEIIIUQuSDBFCCGEEEIIIYQQIhckmCKEEEIIIYQQQgiRCxJMEUIIIYQQQogCsHjxYlxdXXF1dS3srggh8pkEU4QQQgghhBBF2n/+8x8lKHHx4sVcHbtx40bl2G3bthVQD4UQrxoJpgghhBBCCCGKtHfeeUfZ3rt3b66O1bS3tLTkrbfeytd+verOnTunBKrOnTtX4M83efJkXF1dcXd3L/DnEsIUCaYIIYQQQgghirSmTZtStWpVAH7//XfS0tJydNz9+/e5fPkyAJ06daJs2bIF1kd9xo4dy40bN7hx48YLfV4hRMEzL+wOiFeLtbW1/GPyEpJxe3nJ2L28ZOxeTjJuL6/iOHbp6elcvnyZRo0aYW7+cn/sV6lU9OnTh6VLlxITE8OJEyfo3LmzyeO0s1j69OlTkF0UQrxiJDNFCCGEEEKIYubBgwcMHz6c0aNHM2LECB48eFDYXXpueZnqs2/fPgDs7Oxo165dgfRLCPFqUqnVanVhd0IIIYQQQgiRP37//Xdmz55NUlKS8piVlRXffPMNPXr0KMSePb8PPviAy5cvY2lpyZkzZyhTpozBtoGBgfTv3x8AT09Pvv32W2VfREQEa9as4cSJEzx8+JC0tDQqVKhA8+bNGTx4MG+88YbB82pW5vnss88YO3YshQjycwAAIABJREFUZ86cYfPmzQQGBhIZGUmtWrX47bffgKzVfJYsWQKgN/PJ3d2dhw8f4uHhwZw5cwgKCmLlypWcPXuWiIgIbG1tad68OaNHj8bFxcXoa5OZmcmePXvw8/Pj2rVrPH36FBsbG9544w169epF3759MTMzfC/99u3brF+/nj///JPHjx+TlpaGvb095cuXp379+rRr147OnTujUql0XgdjfHx8ePfdd5Wfb9y4gb+/PxcuXCAoKIjo6GhKliypvPaenp56z7tr1y6mTJli8vmKW3aZKNpe7nw/IYQQQgghBAAJCQn88MMP+Pn5KV94NZKTk5k6dSoBAQFMnDiR0qVLF1Ivn0+fPn24fPkyKSkpHDhwgPfff99gW0NTfI4fP8748eNJSEjQaR8SEkJISAi7d+/ms88+w8vLy2R/5s+fz4oVK/JwJdkdOHCASZMmkZycrDwWHh6On58f/v7+rFixglatWuk9NioqitGjRxMYGKjzeGRkJKdOneLUqVNs3bqV5cuXY2dnl+14Pz8/Jk2alK0WTWhoKKGhofzzzz9s2bKFixcv5vm9c+7cOT766KNsj6elpXH37l3u3r3Ljh07mDJlit52QhQ1EkwRQgghhBDiJXft2jWmTJmiTOd5Nvlc87Ovry+XL1/Gx8fHaPZFUdWzZ09mz55NWloae/fuNRhMSU9PZ//+/QA4OzvToEEDAP7++2+8vLxIS0vDwsKCIUOG0KFDBywsLAgMDGTFihVERESwaNEiypUrh6enp8G+HDp0iJs3b1K3bl0++ugjatWqRWJiInfu3Mn1dd24cYP9+/fj5OTE8OHDqVu3Lmlpafj7+7NmzRpSUlKYMmUKhw4dwsLCItu1jho1iitXrgDQpk0bBg4cSKVKlXj06BEbN24kICCAy5cvM2rUKDZv3kyJEiWU48PDw/nmm29IS0vDwcEBT09PGjVqhK2tLUlJSQQHBxMQEMCRI0d0nnffvn1cvXqVr7/+GoDZs2dTv359nTZOTk7KdkZGBtbW1nTq1ImWLVvi7OxM6dKlCQ8P59q1a6xbt47IyEhmz55NzZo1adOmjXJsly5dcHNzY+HChRw5coQKFSqwcuXKXL/OQuQnCaYIIYQQQgjxksrMzGTTpk0sXryYjIyMHB3z4MEDhg4dytixYxk0aJDRqR9Fja2tLR06dMDf35/z58/z5MkTnS/sGqdOnSIqKgrQzUr5z3/+Q1paGiVLlmTlypU0b95c2dewYUO6detG//79CQ0NZd68efTs2RN7e3u9fbl58yZt27Zl+fLllCxZUnm8RYsWub6uf//9l/r167NmzRqdqUtNmzbFzs6On376icePH/PHH3/QrVs3nWO3bNmiBFI++OADZsyYoexzc3Oja9euTJkyhd27dxMYGMiWLVv48MMPlTbHjx9XpoStWbMm23Sipk2b8v777xMXF4eVlZXyuIuLC9HR0crPVatWNToVqU6dOhw/fhwbG5ts+zp06ICnpyeenp5cu3aNJUuW6ARTbGxslP8ASpYsaXLakxAF7eX5yymEEEIIIYRQREZG8sUXX7Bw4UIyMzNzdWxmZiYLFy5k3LhxREZGFlAPC0bfvn2BrGvQFJh9lmaKj0qlUgrXBgYG8s8//wAwYMAAnUCKhpOTE5MnTwaypkbt3LnTYD9KlCjBzJkzdQIpz2P27Nl6a8AMGjRIeY4LFy5k279x40YAHB0d9dYVUalUTJ06VQkKadprREREAFmBKmMBirJlyz5X4M3e3l5vIEWjTJkyfP755wBcvHhRCYYJUVRJZop4Ya5fv8769es5e/Ys4eHhlC1bltq1a+Ph4UGfPn2yze0VBefKlSucOHFCKf4VExNDyZIlcXJy4s033+SDDz6gXr16OTpXWloaW7duxdfXl+DgYJKTk3FycqJdu3YMGTKEatWqFfDVCIDx48cr6cwAR44coWrVqgbby7gVrpCQEHbs2MHx48d5/PgxSUlJODg4UK1aNVq1asXbb79tcPxk7ApHamoqu3bt4uDBg9y4cYOnT59SsmRJKleuTNOmTRk0aBB16tQxeg4Zu/wTGRnJlStXuHLlClevXuXq1avExMQA/ysKmlPx8fFs2LCBgwcPEhISQkZGBlWqVKFz58589NFHODg45Og858+fZ9OmTVy6dInIyEjKlSuHm5sb/fr1y9ESvrl14cIFJk+erFx3bteU0LQ/c+YMAwYMwMfHh2bNmuV7PwtChw4dsLW1JSYmhr179/LJJ5/o7I+Pj+fo0aMANGvWjCpVqgBZ16phrNZKt27dKFeuHLGxsQQEBGQ7v0aTJk2oVKnS814OkFXM1VAgo0yZMlSvXp1bt25lW5UpNDRUmVbUs2dPncyRZ8/Rq1cv1q9fz+3btwkLC6NChQpAVhAGICYmhmPHjtGpU6d8uSZTkpOTiYyMJCkpSQkGak8/unHjhsEaMUIUBRJMES/Exo0b8fHx0SlqFRkZSWRkJAEBAezbt48lS5YY/AdA5J8PP/xQ712NtLQ07ty5w507d9i2bRtDhw5l0qRJRoNcERERfPLJJ/z77786j2uKiO3cuZN58+bRpUuXfL8O8T/Hjx/XCaSYIuNWuH799VcWLVpESkqKzuOPHj3i0aNHnDt3DisrK4YOHZrtWBm7wvHgwQM++eSTbHUQ0tLSCAoKIigoiB07dvD5558zevRoveeQsctfrVu3zpfz3L59m1GjRhESEqLz+K1bt7h16xbbt29nyZIlNGnSxOh55s+fz3//+1+dgEZ4eDjHjh3j2LFj9O3bFx8fn3ydTrNkyRIlkPK8oqOjWbJkCWvWrMmX8xU0CwsLevTowZYtW7h58ybXr1/XCWYePnxYmbaiPcUnKCgIgFKlShnNwDA3N6devXqcOXOGmzdvGmyXk9VscsrZ2dno/nLlygFkK5p769YtZVtTF8YQ7f23bt1Sginu7u7Y2Njw9OlTxowZQ6tWrejUqRNvvvkmrq6u+fq+jY+PZ+3atfz+++/cvn3baEaV9hQiIYoiCaaIAnf06FG+//571Go1FSpUYMyYMbi5uREZGcmGDRuUCuOTJk1i0aJFhd3dYi8sLAzISmPt0aMHzZo1w8nJidTUVM6fP8/q1auJiYlh9erVmJub4+3trfc86enpeHl5KV8MevXqxXvvvUfp0qW5cOECy5YtIz4+ngkTJrBp0ybc3Nxe2DW+SpKSkpg+fToADg4OJlO1ZdwK14IFC1i+fDkA9erV47333sPV1RVra2siIyO5evUqBw8e1BvElLErHGlpaYwaNUoJpLi6ujJs2DCcnZ1JSEjgr7/+YvXq1SQmJrJgwQKqVq1K7969dc4hY1ewKleuTI0aNTh16lSujouLi2P06NGEhISgUqkYNGgQ3bt3x9zcnJMnT/Lrr78SGRnJmDFj2LVrl5Ld8KwNGzYoq7k4OzszevRoatSowcOHD1m5ciVXr15lz5492NvbM2nSpOe+Xo3GjRvz999/59v5TAWMipq+ffuyZcsWIGtKj3YwRTPFx9LSUmcpaE3wydbW1mSAoHz58gDExsYabFO2bNm8dV4PUzcUNf19Nvig3T9TWVSaDJRnj7Ozs2PZsmWMHz+esLAwzpw5o2TxlCtXjrZt29K/f39atmyZs4sxICQkhCFDhvDw4cMctX/2poMQRY0EU0SBSktLY9asWajVamxsbNiyZYvOh5EOHTrwxRdfcOjQIQ4ePMjZs2clna+A1axZky+//JKuXbvqpFJCVoGxXr16MWDAACIiIli9ejX9+/fntddey3aeXbt2cfnyZQCGDh2qM0e3UaNGNG/enEGDBpGSksLs2bPZtGlTwV7YK2rRokU8fPiQli1bUqlSJXbv3m20vYxb4Tlx4oQSSBk9ejTjxo3LFjRp164dn376KampqdmOl7ErHP7+/srd7MaNG7Nx40adv51t2rTB3d2dAQMGkJaWxvLly7MFU2Ts8p+Xlxf169enfv36lC9fngcPHuR6Ks2vv/7K/fv3Afj66691lmJt2rQpbm5ueHl5ERMTw8KFC5k3b162c0RHR7NgwQIAqlWrxtatW5UMggYNGuDu7s5HH33E5cuXWbt2Lf369aNGjRp5vWwdnTt3Zv369flyLsjKTniZNG7cmNdff5179+7h5+eHt7c3ZmZmhIWFERAQAECnTp30Bjzya2r5s5+jXmbNmjXj8OHDHDhwgOPHj3P+/HnCw8OJjY3Fz88PPz8/3nnnHebMmZPn6/7qq694+PAhKpWK999/n169elGjRg3s7OyUFYpCQkKUDL3cTl0T4kWTArSiQB06dEiZ2zl69Ohsd3XMzMyYOnUq5uZZcb1Vq1a98D6+apYvX06PHj0M/kNYrVo1Pv30UyDrbuqzy+BpaMbKzs6OCRMmZNvfoEED+vXrB8Bff/2lVJkX+UezjGDJkiWZNm1ajo6RcSscmZmZyuoKHTt2ZPz48UY/zD+77CXI2BUWTRAEYOTIkXr/drq5udGxY0cgK3U+Pj5eZ7+MXf77/PPP6dSpk5I9kFupqals2LAByFqRZPDgwdnadOnShQ4dOgBZywlrMju1bd++XRlvb29vJZCiYWlpyTfffANkLQu7bt26PPVXn3r16inTNJ5XhQoVclwrrSjRFJZ98uQJ586dA7LGSpO9oT3FB7IyUiArCGaqYK+mKOuzY1rUaPfPVHZqeHi43uM0SpUqRd++fVmwYAGnTp3iwIEDfPXVV0pdmL179+Y5gHf79m0uXrwIZH0nmDlzJq1ataJixYo6/+YZywQSoqiRYIooUP7+/kDWHQBN5fVnVaxYUZn7fPbs2WwfQsWLp13dXnPXTtvt27cJDg4G4K233sLS0lLveTw8PJTtw4cP53MvX22ZmZl8++23pKen8/HHH+foTqeMW+E5efKkUpPBUE0NY2TsCo92rS9jBWK192kfI2NXNAUEBCifN4wVwddeNUZT0FSb5nOOjY2NwcyYBg0aULNmTSCrOHh+3W1XqVT5Vti2c+fOL+VCANrBEs3UHs3/7e3tad++vU77WrVqAVmFT43VQklPT1em5RX15Xdr166tbJsKxGrv1z7OEGdnZ0aMGMG2bdsoXbo0AAcOHNBpk9P3jSbDD7L+Fhpiauray/g+FcWXBFNEgdJEoJ2dnY3O43zzzTeBrA+gV69efSF9E4ZpfxHQdxdWM67wv7HTp27dulhbW2c7Rjy/jRs3cvXqVapVq5bjL+cyboVH8+HTzs6Oxo0bK49HRkZy7949k0FkGbvCU716dWX72SKl2jT7ypUrh52dnfK4jF3RlNNx0d737LikpqYqX/waNmxodHlczSo5YWFh2VZjeR75NTWnIFYbehGqVaum1Ho5dOgQf//9N9euXQOyahNpMp81tAsX79q1y+B5Dx8+rNRXed46IQWtYsWKyg2V/fv3k5ycrLddYmKiUqy+Zs2aucpqqlChghIQfLYorHaAWN8UVY309HRl21AfMzMz2b59u9G+aLJYjD2XEC+KBFNEgYmPj+fJkycAJu+aa+9/drUE8eKdP39e2dY3dtpjZGxszc3NlXort2/fzscevtqePHmizNH/9ttvKVWqVI6Ok3ErPJq7gS4uLqjVatavX0/nzp1p3bo13bp1o2nTpvTp04ft27frTT2XsSs8vXv3Vu7I/t///R8ZGRnZ2vz777/88ccfAMp0HQ0Zu6Ipp+Pi6Oio1Nx4dlzu3r2rvB9y8zknP8e3QYMGOsG7vLC3tze5CkxRpslOiY+PZ/LkycrjmilA2ho2bKhMZ9q0aZPe1Q1DQ0OZM2cOkDXt5b333iuIbuerDz/8EMiaxqPp+7NmzpypTAPStNc4efKkzhSgZ4WGhiqZJVWrVtXZp13UVl82s8brr7+ubBuq7/bzzz+bzEzRPF9kZKRks4tCJwVoRYEJDQ1Vtp2cnIy2rVixorKtCcCIwpGcnKzM6S5ZsqTeu1XaY6Q9dvo4OTlx/fp1oqOjSU1N1VsLQuTO999/T0JCAt27d1fm8+eEjFvhyMzMVL642draMnbsWL3TOK5fv87UqVM5duwYCxcu1HnNZewKj729PXPnzuXLL7/k4sWLvP/++wwZMoTq1auTmJjIxYsXWbVqFWlpaTRv3pwxY8boHC9jVzRpxsXa2trkiixOTk7ExcXpfK7RPoemjalz6DvueZUoUQJ3d3d27tyZ53O4u7vn69K3L1rPnj2ZNWsWqampyjLBzs7OBgNEM2bMUApGDx8+nKFDh9K+fXssLCwIDAxkxYoVSmBh4sSJ2Nvbv7BryasBAwbw22+/ceXKFTZv3syDBw8YMGAAlSpV4vHjx2zcuFFZnadBgwYMGDBA53g/Pz/GjBlD27ZtadOmDbVr18bGxoa4uDilPltiYqLyXNoqV66Mk5MTT548YdWqVTg5OeHs7KxkNjs4OFCmTBnq1atHrVq1CAoKYvPmzcTFxfHOO+9Qvnx5QkJC2LFjBydPnqRx48ZcunTJ4LVqMpEyMzOZNm0agwcP1gkoagdthChoEkwRBSYhIUHZNrXcmya1GVD+WIvCsXDhQiUFedCgQXo//GuPrfbY6aM99gkJCfLl4Dn5+/vj7++PtbU1X3/9da6OlXErHHFxcUq2yR9//EFKSgrVq1dn0qRJNG/eHJVKxfnz55k7dy537tzhyJEj/PTTTzorvsjYFa6uXbuyc+dOfv31V/bs2ZNteVtHR0cmTJhA//79s73eMnZFk2ZcTI0J/G9ctMfy2Z9Nfc7R3p/fn3M6d+78XMGUl3WKj4aNjQ2dOnXi4MGDymPPFp7V5ubmxtKlSxk/fjwJCQmsWLFCWdpaw8zMDC8vLzw9PQus3/nJ3NycFStWMHr0aAIDAzl58iQnT57M1q5Ro0YsW7ZM7xTutLQ0jh07xrFjx/Q+h+Y16dq1a7Z9o0aNYvr06Tx48EBZxEDDx8eHd999F5VKxdy5cxk2bBhPnz7F19cXX19fnbZNmzZl2rRperOKNFq2bEmjRo24fPmy3nPcuHHD4LFC5DcJpogCoz2X0dg8YtBducLQPEpR8A4cOMDq1auBrDoB48eP19suJSVF2c7N2GofJ3IvPj6e77//HshaycLUndBnybgVjqSkJGU7JSUFR0dHNm/erHO3s2PHjjRo0IA+ffoQFhbGxo0bGTZsmDLGMnaFKzU1ld27dytTeZ4VHh7O3r17cXZ2pk2bNjr7ZOyKJs3ra2pM4H/j8uyY5HVs8/tzTpMmTbCxseHp06e5PtbGxkanjtPL6p133lGCKSqVyuiXcYAOHTpw8OBB1q5dy/Hjx3n48CHp6ek4OjrSokULPD09qVu37ovoer6xt7dn8+bN7NmzBz8/P65du0ZcXBxly5bljTfeoHfv3vTt21dvFtLkyZNp1aoVAQEB3Lhxg/DwcKKjozE3N6dy5co0a9aMAQMGGHxNBg0aRPny5dmyZQvXrl3j6dOnOjVSNNzc3Ni9ezfLly/n1KlTREREUKZMGZydnXn77bf54IMPePz4sdHrNDMzY+XKlfz6668cO3aM+/fvk5SUJMsoi0IhwRRRYLQ/OGgXNNVHO/CS0/oPIn9dunRJudtqY2PD4sWLDd5p0y42lpaWZnB1CtAdW2PthGkLFy7kyZMnuLq66l3G0xQZt8LxbHbBxx9/rDdt3N7entGjRzNjxgzS0tI4dOgQH330ESBjV5gSEhL4+OOPuXjxIubm5owaNQoPDw+qVKlCcnIyFy5c4OeffyYwMJCRI0cya9YsndXrZOyKJs3ra+rzCfxvXJ4dk2fHNifngPz/nGNubk7Hjh2VVWxyo1OnTtmKtL6MunTpkuuMBEdHR7y9vfH29s718+XmucaOHcvYsWMN7te3SpQ+OVmSuESJErz33nu5rvNia2tLnz59jGb0mNKtWze6detmsl3VqlWZOXOm0f2mXt8yZcowbtw4xo0bl+t+CpGfXt4JkqLI0xTsA907s/pop7zmJOVW5K9bt24xatQokpOTKVWqFMuWLTO6FKD22JpKV9Yee+3jRO5cuXKFjRs3olKp+O677/L04VfGrXCUKVNG5+dnMxe0tW3bVtnWXtlMxq7wLFq0SFnFZfbs2UyYMAFnZ2csLCywsbHB3d2dzZs3U7t2bdLT05k2bRphYWHK8TJ2RZPm9c3JlBvNuDw7Jrn5nKO9vyA+53Tp0iVPx73sU3yEEKIwvfyhaFFk5aaobG6K1Yr8FRISwvDhw4mNjaVkyZIsWrRIWcLREO0xCg0NNbqSgGbs7ezsZP7/c1i1ahWZmZnUqVOHx48f4+fnl62N9nKbx44dw97enpIlSyp3imTcCoeFhQX29vZERUUBUKlSJYNttfdp2oOMXWHJzMxkz549QFZBS0N3ba2trRk5ciQTJ04kOTmZ/fv3M3ToUEDGrqhycnIiMDCQxMREZSqEIZpxebaGWG6KyuamWG1etGrVipkzZxIbG5vjY8qVK0erVq3yvS9CCPGqkGCKKDBlypRRqnubWu44p0sUivwVHh7O8OHDCQsLw8zMjB9++CFHq8M8u5R1nTp19LbLyMhQlsmrWbNm/nT6FaVJEb9+/ToTJkww2V6TQlu2bFklmCLjVnhq1arFn3/+CaB3aV0N7X3a2UcydoUjMjKSmJgYAN544w2jbTXLrQIEBwcr2zJ2RdOz49KwYUO97cLDw4mLiwOyj0v16tUpUaIEGRkZJj/naL8nCmJ8VSoVPXr0yPfzCiGEMEym+YgCpVm+LDg4WFnbXp8LFy4AWQXc6tev/0L69qqLiYlh+PDhyof3GTNm0LNnzxwdqxlX+N/Y6fPPP/8oKdTax4jCIeNWeLSzvUJCQgy20/w+gu5dcBm7wqG94oWxIBigU2xROxAmY1c05XRctPc9Oy4WFha4ubkBEBgYaLRuiuY8FSpUoGrVqnnqsxBCiKJFgimiQGnm8KrVaiVV+llhYWGcPn0ayEpTfba+gMh/CQkJjBw5kps3bwJZVdz79euX4+Nr1qyJs7MzAPv37ze46sTu3buVbX1L6Ymc++WXX7hx44bR/zw8PJT2R44c4caNGzpfBGTcCo92Ub7Dhw8bbKe9T/uLm4xd4bC1tVX+Tbp8+bLRgIr275r2l2UZu6KpZcuWytju2bPH4EogmnExMzPD3d09237N55ynT59y5MgRvee4cuUKQUFBQFaNEpVK9dz9F0IIUfgkmCIKVNeuXalSpQoAK1as4OHDhzr7MzMz+f7775U7esOHD3/hfXzVpKam4uXlRWBgIABeXl4MGzYs1+fRjFV0dDTz58/Ptv/q1ats374dgKZNm9KgQYPn6LXILzJuheONN95QisuuW7eOW7duZWtz+/ZtVq5cCWTVzOjevbvOfhm7F8/MzEyZ+hgaGsqKFSv0tnv8+DHLli0DsqZbtG/fXme/jF3RY2FhgaenJwA3b97Uu1KKv78/x48fB6B3795UqFAhW5t+/fopQZkff/wxW82S1NRUZs2aBWRlOmlW6BJCCPHyK/Hdd999V9idEMVXiRIlqFatGvv37yc5OZnff/8dKysr1Go1165dY+bMmRw7dgyA7t27M3LkyELucfH3xRdfKB8OO3XqxODBg4mMjDT4X2JiIuXKlct2HldXV86cOcOTJ0+4fPkyd+/epWzZskRFReHn58c333xDcnIylpaWLFy4UO+HUJG//P39uX79OgBDhgzBxsYmWxsZt8Lj5ubGb7/9RkJCAr6+vqjVaszNzXny5An79u3j66+/Jj4+HoBZs2bp1OAAGbvCUqtWLXbt2kV6ejrnzp3j+vXrWFhYkJKSwv379/H19WXKlCnKVNZ33303W6afjF3+u3DhAgEBAVy7do1r167x999/K1muZcuWJSUlRdl37949atWqle0cbm5uHDhwgNjYWE6dOkV0dDRWVlaEhoaydetWZs+eTWZmJra2tixYsEDv31QrKytKly7NiRMnePr0Kf7+/pQpU4a0tDQuX77MN998w5UrVwAYNmwYb7/9dsG+MEIIIV4YldpQXqMQ+Wjjxo34+PgYnE/ctm1blixZgpWV1Qvu2avH1dU1V+2bN2+u944dQEREBJ988gn//vuv3v3W1tbMmzcvz0s2ityZPHmykpJ+5MgRg/PyZdwKz7lz5/jiiy+Ijo7Wu9/c3JwpU6Yod8yfJWNXOE6ePIm3t7dSjNaQ7t278+OPP+pdiUfGLn9p/70zpUqVKhw9elTvvtu3bzNq1CiDtYwcHBxYsmSJyTo28+fP57///a/B6UJ9+/bFx8cHMzNJChdCiOJCginihbl+/Trr1q0jICCA8PBwypQpg4uLCx4eHvTp00fmEL8g+RlMgawU5q1bt+Ln50dwcDDJyck4OTnRrl07hgwZQrVq1Z63yyKHchpMARm3whQZGcm6des4evQoDx8+JCMjAycnJ1q2bMmQIUNMrmgmY1c4oqKi2L59OydPniQoKIi4uDgsLCyoUKECDRo0wMPDg9atWxs9h4xd/smvYApAfHw8GzZs4ODBg9y/f5/MzEwqV65M586dGTJkCA4ODjl6nvPnz7Nx40YuXbpEZGQktra21KtXj/79+9O5c+ccnUMIIcTLQ4IpQgghhBBCCCGEELkguYZCCCGEEEIIIYQQuSDBFCGEEEIIIYQQQohckGCKEEIIIYQQQgghRC5IMEUIIYQQQgghhBAiFySYIoQQQgghhBBCCJELEkwRQgghhBBCCCGEyAUJpgghhBBCCCGEEELkggRThBBCCCGEEEIIIXJBgilCCCGEEEIIIYQQuSDBFCGEEEIIIYQQQohckGCKEEIIIYQQQgghRC5IMEUIIYQQQgghhBAiFySYIoTIlcGDB+Pq6oqrq2thd0Xkkbu7O66urri7uxd2V4o9ze/K4MGD9e5fvHix0ubcuXMvpE+vwu+wvMeFEEIIUdDMC7sDQoiCd+PGDQ4ePMiZM2fknMLFAAAgAElEQVR49OgR0dHRlCpVCnt7e9zc3GjXrh09evSgVKlShd1V8RJJTk5m9erV+Pr6EhISgqWlJQ0aNGDEiBG0bt3a5PEXLlzA09MTS0tLfH19qVat2gvotXgZ+fv7c+3aNQCGDBmCjY1NIfdICCGEEK86CaYIUYyFhYUxd+5c/Pz8UKvVOvtSU1N5+vQpd+/exdfXlwULFvDll1/yzjvvFFJvxcskOTmZoUOHcunSJeWxlJQUTp06xenTp5k6dSqenp4Gj09NTWXatGmo1Wq8vLwkkCKM8vf3Z/fu3QB4eHhIMEUIIYQQhU6CKUIUU7du3eKTTz7h8ePHAJQsWZK2bdvSsmVLHB0dSUpKIjg4mMOHD3Pv3j2ePHnCxIkTuXbtGl999RUqlaqQr0AUZUuXLlUCKe3bt6d79+7ExsayevVqwsPD8fHxoVWrVtSsWVPv8StXriQoKAgXFxeGDRv2IrsuhBBCCCHEc5NgihDFUEREBMOGDSM8PByARo0a4ePjQ40aNbK1/fLLL9mwYQM//PADaWlprFq1ijJlyuDl5fWiuy1eEpmZmWzduhWAjh07snz5ciX41q1bN3r27Elqaipbt27l66+/znb8/fv3WbZsGSqVihkzZlCyZMkX2n8hhBBCCCGelxSgFaIYmjRpkhJIady4MatXr9YbSAEwMzPjo48+Yv78+coX4qVLl3Lx4sUX1l/xcgkODiY2NhaAQYMG6WQxVatWjU6dOgEQGBio9/jvvvuOlJQU+vfvT+PGjQu+w0IIIYQQQuQzyUwRopi5cOECp06dAsDKyop58+ZhbW1t8rhu3brRv39/tm7dSkZGBkuWLGHVqlUmj0tPT2fbtm3s27eP4OBgEhMTqVSpEu3atWPEiBFUqlTJ6PGhoaFs2bKFM2fOEBwcTEJCAtbW1tjZ2VG+fHnq169P165dadasmcFzZGZmcuDAAQ4dOsSVK1eIjIzEzMyMihUr0qJFCwYNGmR05ZLFixezZMkSANatW0eLFi04e/Ys27dv5/Lly4SHh5OamsqRI0eYM2cOhw8fBmDv3r0mV0RJT0+nffv2REZG4uDgwIkTJzA3z/6nNyEhge3bt3P8+HFu3bpFTEwM1tbWVKtWjfbt2zN48GDs7e2NPhdAVFQUq1ev5siRIzx69AgLCwuqVatGz549GTRoEFZWVibPYUpMTIyyXbVq1Wz7NfVPtNtp7Nu3j9OnT1O+fHm8vb2fuy/GpKen4+vry9GjR/n777+JiooiPT0dBwcHXF1dad26NW+//TYODg4Gz3Hr1i127NhBQEAAjx8/JjExETs7O+rVq0fPnj3p3bs3ZmZF575EYmIi69at4+DBg9y7dw+VSkXlypXp2rUrnp6eOXoPaXv06BFbt27l9OnTPHjwgPj4eMqVK4eLiwtdu3bl/fffx8LCwuDx6enpnD17ltOnTxMYGMjdu3eJi4ujZMmSVKhQgcaNG/Pee+/x5ptv6j1+8uTJSq0Ujc6dO2dr5+HhwZw5c4z2Y+fOnezZs4c7d+6QlJSEk5MTbdu2ZeTIkTg5OeXwFRFCCCGEyCLBFCGKmfXr1yvbHh4euSrs6eXlxc6dO0lPT+f06dMEBQVRq1Ytg+1jY2MZPXp0tiyWu3fvcvfuXXbt2sWCBQvo0KGD3uP/+OMPxo8fT2Jios7jT58+5enTp9y7d4+//vqLnTt3cuHCBb3nuH//Pp9//rmy0oe24OBggoOD2bZtG6NHj+aLL74w9RKgVquZMWMGGzdu1Lu/T58+OsGUiRMnGj3f6dOniYyMBKBXr156AynHjx9nypQpSjuN2NhYYmNj+fvvv1m7di3z5s3T+0VS49KlS4wZM4bo6GjlsaSkJOUcu3fvZsWKFUb7mxPaARl9ARPNY8+uDhUbG6t84Z0yZUqBFhG9evUqEyZM4P79+9n2PXnyhCdPnnD8+HGOHDmi8zujkZ6ezpw5c9i4cSOZmZk6+8LCwggLC+PYsWNs2LCBpUuX4ujoWGDXklN3795lxIgRPHjwQOfxmzdvcvPmTXbu3Mny5ctzfL4VK1awZMkSUlNTdR6PiIggIiKCM2fOsHbtWpYvX46zs7PecwwbNow///wz2+NpaWnK34ndu3fj4eHBjBkzjAZm8ioqKgovL69sf6fu3bvHvXv38PX1ZdWqVbi5ueX7cwshhBCi+JJgihDFiFqtJiAgQPm5b9++uTq+YsWKtGzZUslsOXPmjNFgytdff83FixepVasWffv2pUqVKoSHh+Pn50dgYCAJCQl89tlnbNq0ifr16+scGxoaqhNI6dixI61bt6ZChQqo1WoiIyO5fv06Z86cIS4uTu/z379/n/79+yvBg6ZNm9KxY0eqVKlCRkYG//zzD7t37yY2NpZffvkFMzMzxo4da/Q1WLlyJSdOnMDR0REPDw9q165NRkYGV65cwcLCgg4dOmBra0tMTAx+fn54e3sbLda7d+9eZbtPnz7Z9h88eJDx48eTkZFByZIlcXd3p3nz5jg4OBAfH8+5c+c4cOCA8lquWrWKVq1aZTvPvXv3+Pjjj4mPjwfAxcWFvn37UqlSJcLCwvDz8+PKlSuMGzeOtLQ0o6+BKdWrV8fc3Jz09HSOHz9O06ZNlX2pqamcPn0aINt758cffyQiIoK2bdvSu3fv5+qDMRcuXGDEiBEkJycD8Nprr/HWW29Ro0YNLCwsCAsL48qVK/zxxx96j1er1YwbN04Jmjk6OtKrVy/q1KlDqVKlePToEX5+fvzzzz8EBgYydOhQduzYkS9ZP3kVExPDkCFDePLkCZCVMfTee+9RvXp1YmJiOHz4MGfOnOGzzz6jbNmyJs83e/Zs1q5dC4CNjQ09e/akfv36lC5dmvDwcPz9/Tl37hx3795l8ODB7N69W29AKSUlBWtra1q1akW9evWoUqUKlpaWhIeHExQUxL59+0hMTGT37t2ULVuWb775Ruf4wYMH06VLF9atW8e5c+cAmDFjRrZsIkMZcOnp6Xz++edcvHiRFi1a0KVLFxwdHQkNDWXHjh3cunWL2NhYJkyYgK+vb4EEc4QQQghRTKmFEMVGUFCQ2sXFRe3i4qJ2c3NTp6am5vocixcvVs4xbty4bPs9PT2V/S4uLuoJEyZke57MzEz13LlzlTa9e/dWZ2Zm6rT59ddflf3//e9/DfYnMzNTff78+WyPZ2RkqD08PJRr3b9/v97jw8PD1X369FG7uLio69Spo75582a2NosWLdK5poEDB6rj4uIM9unbb79V2p49e9Zgu/j4eHXDhg3VLi4u6rfeeivb/kePHqmbNGmidnFxUXfs2FF9/fp1vecJDAxUN23aVO3i4qJu37693nEdMmSI0qfJkyer09LSdPZnZmaqfXx8dK6zU6dOBvtuypgxY9QuLi7q+vXrq7dt26aOiYlR37t3Tz127Fjl/IcPH1ba//XXX2pXV1d1gwYN1Pfv38/z85ry9OlTdZs2bZQ+/PDDD9leC43ExET1iRMnsj2+Zs0a5Xhvb291YmJitjaZmZnq+fPnK+3mzZun9zk0+z09PfXu137vBQQE5OJKdX399dfKeYYPH663z9rXpflPn8OHDyv7hw4dqo6KitLbbvPmzUb/VqjVavWZM2fUSUlJBvsdFRWlHjhwoPL7aei9MWnSJOW5QkJCDJ5Po1OnTjrXuWXLlmxtkpOT1f369VPa+Pn5mTyvEEIIIYRG0ZnoLYR4bpq70pB1Zzovq6Rop+uHhoYabVu1alVmz56d7XlUKhUTJ06kUaNGQNY0A022i8a9e/eU7f79+xt8DpVKpbdeir+/P//88w8AX331FW+99Zbe48uXL8+CBQsoUaIEmZmZrFu3zug1WVtbs2DBAsqUKWOwjXaGiXbmybMOHz5MUlISAO+88062/StXriQ+Pp4SJUrwyy+/GKy/0qBBAyZPngxkjfGBAwd09l+7do2zZ88CWVkj06dPzzadSKVSMWnSJBo0aGCwv7kxceJESpcuTUpKClOnTqV58+Z07dqVgwcPAlmZRpopSWlpaUybNg21Ws2nn36aq6lnubVx40al+HLv3r2ZOHGi3qlVkDVdqV27djqPpaSkKFOh6tevz9y5c/VmnKhUKsaPH6+8Nzdv3kxKSkp+XkqORUZG8ttvvwFga2vLTz/9pLfPQ4YMoXv37ibPt2jRIiAr22Pp0qXY2dnpbTdgwADld+HgwYPKMuzaWrVqlW26lzY7Ozvmzp0LZNU+2rdvn8n+5dZ7773HBx98kO1xS0tLxo0bp/z87N8oIYQQQghjJJgiRDGiWWEFyHM9Cu0pAPrqYWgbNGgQlpaWevepVCqGDh2q/KyZMqGh/WXv1q1bue6n5stjmTJljAZjICtApAkiaKagGNKtWzcqVqxotE3Tpk2VgMChQ4cMfonWBFpUKhVvv/22zj61Wq18cWzVqhVvvPGG0efs2bOnEhR49hq0X9vBgwcbnKqgUqkYNmyY0efJKWdnZ9avX0/dunV1Hjc3N2fgwIEsWrRImf60evVqbt68Se3atRk+fDiQdf27du2iX79+NG7cmCZNmuDp6Ym/v/9z9UvzmpqZmel8Uc6pkydPKrVrhg4darK4rCZIFh8fz+XLl3P9fPnhjz/+UKZuvfvuu9ja2hps+/HHHxs91/Xr17lx4wYAAwcONFm8WnP9GRkZSkAvt6pVq6ZMEbpy5UqezmHMRx99ZHBfs2bNlN+r27dv5/tzCyGEEKL4kpopQog801e7Q1vLli2V7b///ltnX+vWrVmzZg0AY8eOZdSoUfTo0SPHq2poCtI6Ojpy8uRJk+01X4ofPnxIcnKywbvlxlYN0vb222/zyy+/EBcXx9GjR7NlxoSFhSn1a5o1a0aVKlV09mtW7AEoXbp0joII1tbWPH36NNuXvqtXryrbpsbE1P7cqFevHrt37+bOnTs8ePAAS0tL3njjDZ1AXkhICEuXLkWlUjF9+nQli2natGls3boVyLr+zMxMzp8/z/nz5/nyyy8ZOXJkrvsTExNDUFAQALVr185TBsxff/2lbMfGxpocF+3srdu3b9OiRYtcP+fz0h5/7d85fTR1TxISEvTu1y70nJqamuvr1yc+Pp69e/dy4sQJbt68SXR0dLai0xra2XX5wcrKyuiKWxYWFtjZ2REeHq4TjBZCCCGEMEWCKUIUI+XKlVO2nz59mqdzaBd7NXaHG7IKexpjZ2eHjY0NT58+JSwsTGdfhw4d6N27N76+vkRFReHj44OPjw/Vq1encePGNGvWjE6dOuldtjYhIUEJRAQHB+Pl5ZXTywOyviQbCqZUqFAhR+d45513+OWXX4CsDJRngyl+fn5kZGQA+gvPPnz4UNk+ePCgMj0mJ54dW+3XNjdjkl9q1KhBjRo19O6bPn06ycnJ9O/fXylUe/jwYbZu3YpKpWLatGl88MEHZGRksHjxYlasWMGCBQto06YN9erVy1U/tL/Y16xZM0/Xoj0uM2bMyNWx+fma5ob2+L/++utG26pUKl577TW9q1+B7vVrlgvPKX3XHxAQgLe3tzL1yhRNAeX8Ymtra7RANKBkcj27apEQQgghhDESTBGiGNHO6nj48CFpaWm5rpsSHBysbJua7pKT1UusrKx4+vSp3jvhP/74Iy1btmTt2rXKVB/t5VJLlCjBW2+9xaRJk3SCHM/7hcvYajbG6jtoc3Z2pmHDhgQGBnLy5EliYmJ0gk+aKT6Wlpb06NEj2/GGVijKiWf7r7nLb25unqPx1oxJQdu/fz8nT56kfPnyeHt7K49r6ta0bduWgQMHAlmZQ+PHj+fw4cPcuXOHDRs24OPjk6vn035fmJqeYkh+jsuLop3lkdPfSUPy8/rv3r3LqFGjlFWVnJ2dad++Pa+//jq2trY6UwS//fZboqKisi1D/bxMTdMSQgghhMgrCaYIUYzUqFFDWbY3JSWFa9eu5brgqHbdhyZNmhhtm5SUZLRQq6YNZE3leJZKpaJfv37069ePkJAQ/vrrLy5evKgsuZqRkYGvry9//fUXO3bsoHz58oDuF+U333yTDRs25Pj68lOfPn0IDAwkLS2N/fv3M2jQICBrusO///4LQKdOnfQuRat9DV5eXnz++ed57ofmXOnp6TkKoGnGpCDFxcUxe/ZsACZPnqxkTaWnpyvvsW7duukco1Kp6NatG8uXL9eZbpJT2u9FQ9NITNEeF39//wItlptftPuck7E11kb7XGvXrjU5bciYFStWKIGU0aNHM27cOINZIlOnTs3z8wghhBBCFAa5ZSNEMaJSqXS+/GiKtOZUWFiYThHJ1q1bG21///59o/ujo6OVDAhT02eqVatG3759mTFjBgcPHmTXrl1KcdPHjx+zcuVKpW3ZsmWVL335XWMhN9566y0lcKG9qo/2665vFR/Qzfp53mvQfm1zMyYF6ccffyQ8PJy2bdvqFN+Njo5WplPoy3zSPGZqJSl9KlasqHxZz2sxUe0+5aUPhUF7/LVXydJHrVYTEhJicH9+vi81f0scHBz44osvDAZS4uPjpV6JEEIIIV46EkwRopgZPHiwsr1r1y6dGgimLFu2jPT0dADatGljsu6EpsCqIefOnVO23dzcctwPyCpu+sMPPyg/axcGhayMFMgqcGrqC2RBsbe3p23btgBcunSJkJAQ1Go1vr6+QFa9hvbt2+s9tm7dukomRUBAwHNNb9DOPjI1JnldcSU3Ll++zLZt27C0tGTatGkG2+nLkMhrRglkvd61atUCsgr8GgsaGKJ5X8HLs1Rubsb/6tWrRqfJaV+/qZWvTImIiACyllA3Nt3m7NmzJt//2oEYtVr9XP0SQgghhMgPEkwRophp1qyZ8gU/MTGRiRMn5ij139/fn82bNwNQokQJxo4da/KYTZs2GS3aqFmtB7JP6cgJ7RVwNEEejb59+yrbixYtyvW584t2cdm9e/dy4cIFJYDVq1cvg1NuSpQooWRsPHz4kO3bt+e5D127dlW2N2zYYLB2h1qtZu3atXl+npxIT0/nP//5D5mZmYwZMyZbQVxbW1vlNdGsvKNNk1GS00LAz9JkAmVmZrJw4cJcH9+hQwfs7OwA2Lx5c7bCyUVRx44dldd09+7dRrM8Vq1aZfRc9evXp3bt2gD8/vvveVq2XENTm0UTZNQnIyOD5cuXmzyX9jTBFzFNTQghhBDCFAmmCFEMzZ07F0dHRyAro2PYsGHcvXtXb9vMzEw2btzIuHHjlC88Xl5eNG7c2OTzhISEMHXq1GyBDrVazfz587l06RIArq6utGnTRqfNkiVLOH36tNE70ps2bVK269Spo7OvR48e1K9fHwBfX19mzZplNLCTnJzMrl278PPzM3ldueHu7q7URNm3b5/OdB9DU3w0Ro8erSwjPHPmTPbs2WO0fWRkJEuXLuX69es6j9epU0eZknXnzh2mT5+urCSkoVarmTdvnk5NnIKwevVqbty4Qa1atfj444+z7S9ZsiSNGjUCYMeOHTpfjMPDw/n9998B3QyJ3Bg4cKASiPH19WXevHnZ3p8aycnJ2bJPrK2t+eyzz4CspZY//vhjg787GoGBgTpZVC+avb29ElyMjo7G29tbqVWibcOGDcrra4hKpWLChAlAVkHZkSNHcuXKFaPHBAUF6c1A0vx+RkVF6Q3ipaWlMXXq1GzLputTtWpVZVtTj0gIIYQQojBJAVohiqHy5cuzatUqRo4cyePHj7l06RK9e/emXbt2tGjRggoVKpCUlERwcDCHDx/W+bI4bNgwPv300xw9T5cuXfjtt9+4du0affv2pVKlSkRGRuLn56cEUiwsLJg1a1a2egnnzp1j8eLFODo60rZtW+rUqYOjoyOZmZmEhYVx9OhRpQiphYUFw4YN0znezMyMxYsX88EHHxAaGsq6dev4/fff6dGjB3Xq1KFs2bIkJiby6NEj/v77bwICAkhMTOSLL754jlc2O0tLS7p3786OHTsIDg7mwYMHQNYStZqggSFOTk7Mnz+fMWPGkJqayqRJk1i9ejXu7u68/vrrlCpViri4OO7evUtgYCAXL14kIyODFi1aZDvXd999x7vvvkt8fDzbt2/nypUr9O3bFycnJyIiIvD19SUwMJAGDRrw5MmTAsm4ePjwIUuXLkWlUjF9+nSDWTkffvgh58+f5/Hjx3z00UcMHDiQtLQ0Vq9eTVJSEmZmZkox39wqW7YsCxYsYPjw4aSkpPDrr79y6NAhevbsSY0aNShZsiQRERFcvXqVP/74gzp16iiZXBqenp5cvXqVPXv2cOPGDXr16oW7uzvNmjVT3qNRUVHcvHmTs2fP8uDBA1577TW++uqrPPU5P3h7e3Py5EmePHnCiRMn6N27N++99x6vv/46sbGxHD58mNOnT1O1alXKli1rcGlkyAoQenl5sXTpUh49ekT//v1p06YNrVu3xsnJCZVKRXR0NEFBQfz5558EBQVRokQJpk+frnMeT09PZaqQj48P586do23bttjZ2XH37l1+++037t69S4sWLbh3757RGi3ataDmzZtHVFQUzs7OlChRAsiq9eLq6vo8L6EQQgghRK5IMEWIYsrFxYVt27bh4+PD77//TlpaGkePHuXo0aN621esWJEJEyboTJ8xxcfHh6ioKC5evKj3znzp0qWZP3++codan/DwcHbv3m1wv52dHT/++KMy9UBbpUqV2LFjB1999RVnz54lPDyc9evXGzxXiRIllIyd/NSnTx927NgB/G95WFNZKRrt2rVjw4YNeHt7ExISwvXr17NlnmiztrbWuzrQ66+/zv/93//x6aefEh0dzY0bN5g7d65Om9q1a/Pzzz/j6emZ00vLlRkzZpCUlES/fv1o1qyZwXZvvfUWJ06cYNeuXVy5ciVb5sPnn39u9D1jSrNmzVi/fj3jx4/n4cOH3L9/3+BUEkNFUefMmcPrr7/OsmXLSE1N5dChQxw6dMjgc2ovS14YbG1tWbNmDSNGjODhw4eEhIRkm+bk5OTE0qVLmTVrlsnzff7551SqVIk5c+YQHx/PqVOnjNaQ0Xf97u7ujBo1ihUrVgDo/fvTpEkTFi5cyPvvv2+0P3Xq1KF37974+voSERGR7b3t4eHBnDlzTF6XEEIIIUR+kWCKEMVYhQoVWLBgAaNHj+bAgQOcOXOGR48eER0dTalSpXBwcKBu3bp06NCBHj16UKpUqVyd38bGhnXr1rFt2zb27dtHcHAwiYmJODk50aFDB0aMGEGlSpX0Hrts2TLOnj3Ln3/+yT///MP9+/eJiYkB/ldItH379rz//vvKVBhD17hmzRr+/PNPfH19uXjxIqGhoSQkJGBlZYWTkxMuLi40b96czp0757kWhzFvvvkmVapU0Sn2q11LxZRGjRpx4MAB9u/fz9GjR7l69SpRUVGkpqZSpkwZqlatSt26dWnVqhUdO3bUWb5WW5MmTdi/fz+rV6/G39+fR48eYWFhQbVq1ejZsycffvihUscivx04cIA//vgDBwcHJk6caLL97NmzadKkCVu2bOH27duoVCrq1q3LsGHD6NKly3P3p2HDhhw4cIA9e/Zw5MgR/v33X6Kjo1GpVDg6OuLi4kK7du3o1auX3uNVKhWffvop77//Ptu3bycgIIDg4GBiYmIwMzPDzs6OGjVq0KhRIzp06GAyC+lFcHZ2xtfXl3Xr1nHgwAHu3buHSqWiSpUqdOnShcGDB2Nvb5/j8/Xr14/u3buzc+dOTp06xc2bN5Xf0XLlylG9enUaNGigZLzpM2HCBJo1a8bGjRsJDAwkPj4eW1tbatasSe/evfHw8MDcPGcfRX744QeaNWvG/v37uXXrFnFxcQancAkhhBBCFDSVWsriCyGE+P/27j6uxvv/A/jrSKIbE6IRiSQscrMSYxPWFpmaNpoy+20m99vYxIaxmbHvzBLNY+RuM5PuUetgzE1FaMpNIlJEulGnVo7T+f3R41yPc3RuOna6Ya/nX5euz/lcn3POdZ2H6319Pu83ERERERHVGRPQEhERERERERHpgcEUIiIiIiIiIiI9MJhCRERERERERKQHBlOIiIiIiIiIiPTAYAoRERERERERkR4YTCEiIiIiIiIi0gODKUREREREREREemAwhYiIiIiIiIhIDwymEBERERERERHpgcEUoiZKLBajV69ecHJywt27dxt7OPWqV69e6NWrF/z9/Rt7KNTERURECOdLRESE1rbHjh1DYGAgXnrpJbzwwgvC67Zt2wYASE5OFv4WHBzcAKM3DF4vjU8mk8HDwwO9evXC//73v8YeDhERETUCBlOImqCqqiqsWrUKAPD222+jY8eO9Xas0tJSBAcHIzg4GGKxuN6OQ9SQNm/ejA8++ACHDx9GQUEBpFJpYw+JniFGRkYIDAwEAISFheHmzZuNPCIiIiJqaM0bewBEVNuvv/6KvLw8mJiY4IMPPqjXY5WWlmLDhg0AAG9vb4wePbpej0dU3woKCvDjjz8CAExNTTFlyhQ4ODigVatWAAAHB4fGHB49I7y8vLBp0ybcuHED69evx/fff9/YQyIiIqIGxGAKURNTWVmJzZs3A6gJbtTnrBSip42Pjw98fHy0tjl58qQwEyUwMBDTp09X287V1RVXrlwx+Bjpv8HIyAjvv/8+Pv/8cxw8eBAzZ86Evb19Yw+LiIiIGgiX+RA1MVFRUSgqKgIATJgwoZFHQ/T0yc/PF7Z79+7diCOhZ93rr78OExMTVFdXY/v27Y09HCIiImpADKYQNTG7d+8GAHTt2hUDBgxo5NEQPX0ePnwobLdo0aIRR0LPOnNzc4wcORIAEBMTA4lE0sgjIiIioobCZT5ETciVK1dw+fJlAMC4ceN0tr99+zYOHz6MlJQUXLlyBffu3YNUKoWFhQXs7e0xfPhwTJ48GRYWFrVem5ubi1GjRqn8LTIyEpGRkbXaHjp0CDY2Nk/0nvLy8k5OtVEAAB3wSURBVLB161YcO3YMd+/ehZmZGezs7DB+/HhMnDgRzZvr/hny9/dHSkoKgJrPSCaTITo6GrGxscjKykJhYSGsra1x+PBhAEBwcLCQB2bHjh1wdXXV2Lc+bRMTE7F3716kp6ejrKwMVlZWGDhwIPz9/dG/f39EREQgKCgIAPDNN9/oXI6ii0wmQ2xsLOLj43Hp0iUUFRVBJBLB0tISbdu2RY8ePTBs2DB4eHjA1NRU5bWLFi0SvkvF9xcTE4OIiAhkZWXhwYMHsLKygpubG6ZNm1bn5Qm3b9/Gnj17cOLECeTm5kIikeC5556Dg4MDxowZg4kTJ9YpgCGXy3Ho0CEkJCQgLS0N9+/fx8OHD9GmTRvY29tjyJAhGDduXK3zTttn3KtXr1rHCQgIUPm3i4sLdu7cCaCmmo9i/+zZszFnzhytYz516hQOHDiAM2fOoKCgAP/88w8sLCzQvXt3vPjii/Dy8vrXyzwMcb0oPHr0CJGRkfjjjz9w6dIllJSUwMzMDJ07d8bw4cPxzjvvoEOHDlr7qKqqQnh4OMRiMa5evYqSkhIYGxvD0tISlpaW6NWrF4YPH45Ro0ap/d7T09Nx9OhRnD17FteuXUNhYaFwDvft2xceHh4YN24cjIyMdL6f6upqREZGIioqCpmZmaisrESHDh3g5uaGgIAA2Nvb63U9l5eXY+/evTh69Kjw3kxNTdGlSxeMGDEC/v7+aNu2rc5xeXl5IT4+HpWVlUhISMCbb76p8zVERET09GMwhagJUa6mo+0mAKi5EZw6dSrkcnmtfUVFRUhJSUFKSgrCwsIQHByMwYMHG3y8uojFYixcuBAVFRXC36qqqlBUVITU1FTExMQgNDRUrz5LSkowc+ZMpKamGnq4GkmlUixYsADx8fEqf8/Ly0NeXh4OHDiABQsWoE2bNgY7ZlFREaZPn44LFy7U2pefn4/8/HxcvHgRsbGxsLCw0Jo4WCqVYtasWbWqNeXl5SE8PBzR0dFYtmwZfH19tY7pp59+woYNG1RmfgDA/fv3cf/+fZw8eRLbt29HaGgo7OzsNPaTk5OD+fPnIyMjo9a+goICFBQU4NSpU/j999+FAFljKioqwieffIKTJ0/W2ldcXIzU1FSkpqYiNDT0X+VgMeT1kp2djcDAQGRnZ6v8vaSkBCUlJcjIyMCOHTuwbNkyjcsJc3Jy8P7779eqVCOVSlFRUYG8vDykp6dj3759iIqKqrWkasOGDRpLTivO4UOHDmH79u3YtGmT1vxQEokEM2bMwOnTp2uNMScnB9HR0fj66681vv5xR48eRVBQEAoLC1X+/uDBAzx48ADp6enYvn071q5dWyvo/DhXV1eIRCIhQMhgChER0X8DgylETYjiZq1Zs2Z44YUXtLatqqqCXC5Hz5494erqiu7du8PS0hJVVVW4c+cOxGIxMjIyUFRUhBkzZiAqKkrlKX+7du0QEhKCwsJCLF26FEDNTcHjT/IVbfWVmpqK+fPnC4lABw4cCE9PT7Rr1w65ubmIiopCamoqFi9erFe/CxcuRGpqKhwdHTF27FjY2NhAIpHUayLRpUuXCoEUExMTeHt7Y8CAAWjWrBnS09MRHh6Ob7/9Fh4eHgY75hdffCEEUmxtbTF27Fh069YNLVu2hEQiQXZ2Ns6cOYO0tDSdfX333XcQi8Xo3LkzfHx80L17d5SUlODQoUM4fvw4pFIpvvjiC7Rt21bjjeOqVauEnBCtW7eGp6cnnJycYGZmhoKCAojFYiQnJ+PGjRvw9/dHZGQkrKysavWTnZ2NSZMmoaSkBABgZWUFT09PODo6olWrVigqKkJ6ejqOHDmi92cWEhICANi/fz8OHDgAAJg3b55K9R59A15FRUXw9fVFbm4uAMDCwgJjx46Fk5MTzM3NUVJSgkuXLuHPP/9UydWiL0NeL/n5+fDz8xNyL9na2sLb2xu2trZ48OABDh8+jGPHjqGiogKLFi1Cs2bNMH78eJU+5HI55s2bJwRSevfuDQ8PD3Tp0gXNmzdHaWkprl27huTkZFy6dEntOCorK9G8eXM4Oztj4MCB6Nq1K8zNzfHgwQPk5uYiJiYGd+/eRUZGBmbNmoXdu3fD2Ni4Vj9yuRwzZ84UAimtW7fGxIkT0bt3b1RXVyM1NRWRkZFYtGgRhg8frvPzSUhIwEcffQSZTAZjY2O4u7vDxcUF7dq1g0QiQXJyMuLj41FeXo7Zs2dj69atcHNz09ifYnbStWvXcOrUKchksjrNtCEiIqKnG4MpRE2ETCYTntT36NED5ubmWtv36NEDMTExapc2AMDMmTMRFxeHhQsXoqysDCEhIfjmm2+E/a1atcLo0aOFm0QA6NSpk0FKI8tkMixZskS4MZw5cybmzZun0ubdd9/Fp59+ioMHD+rV97FjxzB16lThJrC+nTp1ChEREQAAS0tL7NixQ+XmfPz48QgICEBAQAASEhIMcszCwkIcOnQIAPDCCy9g586dtZbxKOTl5ensTywWw9XVFZs2bYKZmZnwdz8/P+zatQsrV66EXC7HsmXLMGTIEJU2itcrAilDhw7F999/D0tLS5U2AQEB+O2337Bs2TIUFBRg1apVWLdunUobmUyGuXPnCoEULy8vrFy5UihZrEwqleKvv/7S+d6UKc5d5Zv7QYMG6Zzlpc1nn30mXCNubm744Ycf1AZkFLMSnoShr5fPP/9cCKR4eHjgu+++U1mCM3nyZERERGDJkiWorq7G8uXLMWTIEJUlP+np6bh48SIAYOTIkQgJCdEYIMjKylIbcH311VcxdepUtUE1AJg7dy7Wrl2LHTt24MKFC4iLi4O3t3etduHh4UhOTgZQk0tq586dsLa2FvZPmDABkyZNwrRp03TOZLpz5w4WL14MmUyGTp06ITQ0tNZvqK+vLwICAvDee++hrKwMixYtglgsVhvoUejfvz+uXbuGiooKXL16FY6OjlrHQURERE8/JqAlaiJu3bqFf/75BwC0LpFQ6Ny5s8ZAisK4ceOEJ84HDhwQbtbq25EjR4TlBS4uLrVuDIGaxKDffPMNOnXqpFffffv2bbBACgBs27ZN2F66dKlKIEXBxsZGJVD1b926dUtYvuXl5aUxkALUnAedO3fW2p+FhQXWrVtXK0gCAFOmTIGnpyeAmiU2sbGxtdr8+OOPAIDnn38eISEhtQIpCpMmTcIbb7wBoObp/507d1T2HzhwAJmZmQBqghxr1qxRG0gBIMwYaExnz57FsWPHANTM7ti4caPGmS0ikeiJA5GGvF4uX74sBKE6d+6MNWvWqM1l4uPjg8mTJwOoyR3yyy+/qOzPyckRtt98802tMy3s7e3VnhP9+vXTGEhRvKdFixYJM+aio6PVttuxY4ewvXbtWpVAioLid0GXLVu2QCKRwMjICBs3btT4G9qvXz+hv/z8/FpL/B7XvXt3YZvltomIiP4bGEwhaiJu374tbD/33HMG61dREaiysrLB/pOfmJgobL/33nsa27Vq1Qp+fn569f3OO+80WCClqqoKJ06cAAB06NABr732msa2rq6uOoNbddWyZUth++rVq/+6v/Hjx2tdqjVt2jRhW/m7A2puzhXnzeTJk7UGdhTHAmpmW5w6dUpln3KgZv78+Q32PT4p5fEGBgbqfO9PypDXi3Jf/v7+KufS495//32IRKJarwMMfw5qYmRkhP79+wMA/v7771o5oHJycoQAXP/+/eHs7KyxLy8vL63LuORyufCdurm56Syb7enpKST8VfwOaKL8m638W05ERETPLi7zIWoiFEsfAP2CKWlpaYiJicH58+eRm5uL8vJyjTNQ8vPzdeZiMQRFro9mzZrpXGKhLReBOg2ZSPfy5cvCZ/niiy/qvPl3cXExSMCqZ8+e6NChA+7du4fw8HDI5XK89dZb6Nev3xMFIHR9xor8HxKJBOnp6Sr7zpw5I2w/fPiwVhLbx929e1fYvnbtmso+RdLg1q1b48UXX6zT2BuTYrwikUgof1sfDHm9/P3338L2sGHDtLbt1KmTkOvj+vXrkEgkwvLCQYMGoWXLlqisrERISAhKS0sxYcIEvZevVFdXQywWIyEhAZcuXcK9e/dQXl6O6urqWm3Ly8shkUhUqo8pn4+6PhtjY2MMHDhQ41IfRcUeADAzM9N5LgOAqampkB9GG+UgTmlpqc5+iYiI6OnHYApRE6FcIUXdcgx17T///HONU+PVkUgkTzQ2fd27dw8A0L59e51P821tbfXqW1vFD0NTvA8A6NKli8722trcvn1byEGhjp2dHXr06AGg5mn9ihUrMGfOHEilUuzbtw/79u1D69at4ezsjEGDBuGll16qc2Csa9euWveLRCJ06dJFKJ/78OFDYWmIck4WRcnZulK+qZRIJML5Z2dnJ8yIaMoUgaF27doZtFLT4wx5vRQUFAjb3bp103nsbt264dq1a5DL5SgoKBCCKW3atEFQUBCWL1+OR48eISwsDGFhYWjXrh0GDBiAwYMHY8SIEcI5q05+fj5mzpyptmqTJo8HUwx5DSqfywkJCXrlONIVIFHOcVVZWVnnfomIiOjpxWAKUROhnNegLkGPFStWCIGUFi1a4OWXX4aTkxM6duyIVq1aCTkOkpKSsHPnTgBQ+zS4PihKu2rKh6GsLm2UaVu2YGjKJWrrclxtN8JJSUkICgrSuH/27NmYM2eO8O+RI0ciPDwcwcHBOHr0KKRSKUpLS3Hs2DEcO3YM69atg4ODAxYuXIgRI0ZoHZe+30N5eblwPpaVlel8rSbKM6TKy8uF7fpaLmNoiuuwvsdryOtF8Tk3b95cba6Uxym/N+XvCKjJgdO9e3ds3LgRycnJqK6uRmFhIcRiMcRiMVavXo0BAwZg8eLF6Nevn8prpVIp/u///g9ZWVkAapI3u7u7w8HBAe3atYOJiYkwy2rHjh1CglmZTKbSj77XoLbPx1Dnsq6+G/I3ioiIiBoPgylETYTyk+8HDx5obZubm4vw8HAAgLW1NXbt2qXxiazysouGYmpqirKyMiGhrjZ1aVOftAWYlG806/K0WfnGzxAcHR0REhICiUSCs2fP4ty5czhz5gzOnTsHqVSKzMxMTJ8+HWvWrKlV2laZvt+D8swo5c9g+/btGDJkyBO9F+U+Df051RdF6eP6Hq8hrxfF5/zo0SOVGUaaKL83dTPiXFxc4OLiguLiYqSmpuLcuXM4ffo0Lly4gOrqapw7dw5+fn7YsmWLyjKc/fv3C4GUYcOGYcOGDRqDUjExMRrHp+81qO3zUe5r1qxZmDt3rs7+6kr5N9uQOa+IiIio6WIwhaiJUK7IoiuYkpSUJCRqnD59ep2ntjeUDh06oKysDPfv30dFRYXWJ/s3b940+PGVbyB1PVEuLi7WuE+5VOytW7d0HldbGx8fH/j4+OjsQx1zc3OMGDFCmIFSXFyM0NBQbNu2DXK5HKtXr8bYsWM1VlzJycnRmhxXLpcLY2/Tpo3K56e8rCo/P/+Jxq94DxYWFigrK0N2djbkcnmTX+rTsWNHlJSUoLCwECUlJfW21MeQ14uVlZVQGvrmzZvo2bOn1vaK/kQikdbKO5aWlhg9erRQseju3btYs2YN4uLiIJVK8e233wolxAHg5MmTwnZQUJDW96QtYashr0FDncvqKOe80rdCGRERET2dmnYpBaL/EBsbG+GGQ1EmVZPCwkJhW1cegePHj2vdr5zQ9PFKGk9KMeW/uroaKSkpWts+XvHFEDTlXFBHOWHn4xwdHWFsbAwAOH36tM5lUrreq6FYWloiKChIyJlSWFiIGzduaGyflJSktb/09HRhScvjeViUE8Xqqmiiy6BBgwDU5J84ffr0v+qrISiSHcvlchw5cqTejmPI60V5uY2u7+vOnTu4fv06gJrSvsp5P3Tp2LEjvv32WyEAk5GRoTJzRPk3SlvOnsLCQly+fFnjfuXzUbEUSBOpVIqzZ89q3N+nTx/hPSYlJRl02aPicwRgsKpeRERE1LQxmELURBgZGaFv374AIFTW0ER5Tb62J7FisVhndRnlJ8aGWnKjeHoNANu2bdPYrrKyErt37zbIMZXZ29sL29oCCampqVqTY5qYmAgVUe7du4f4+HiNbZOTkxus9LSC8mymx3NNKIuJiUFRUZHG/crf0auvvqqyz8nJSZjdcPDgwX9VJld5KdIPP/zQYDl8npTyeENDQ+ttuY8hr5cxY8YI27t27UJVVZXGtlu2bBG+g8e/97po3ry5ymyPR48eCdvKv1E5OTka+/jpp5+0zh7r2rUrHBwcANRULjt//rzGtrGxsSozRB5nZGQELy8vADUz9vbu3auxrb7S0tIA1CyVUv79ISIiomcXgylETcjQoUMB1DyhVpRLVcfJyUnY3rJli9plQWlpaViyZInOY7Zp00aYyXHp0iWDzE4ZOXIk7OzsANQ8SVdXBUYqlWLJkiX1sgzJ2dkZrVu3BgAcOHCgVrlfoOYGb+HChTr7evfdd4XtFStWIDMzs1ab3Nxcrcll9fXXX39h+/btWhNm3rx5U1hKYWpqqvXpf2lpKT7++GO1wYDdu3cjLi4OQM0SkXHjxqnsF4lE+PjjjwHUfGfTp0/XOpsHALKysrBs2bJaf3/ttdeEp/apqan49NNPNQbwHj16hD///FPrceqbs7MzXn75ZQDAjRs3MGvWLI0363K5HIcOHXqi4xjyenF0dBSWg926dQtBQUEqlcIUoqKi8MsvvwCoCQD4+fmp7I+JicG+ffu05ik5f/68sKSoS5cuKjNblH+j1q9frzZwtmfPHiE5tjYBAQHC9sKFC9Xmgbp48SJWr16ts68ZM2YIvw1fffUVoqKitLYvLCxESEiI1tkziqVrQE3pak3L7YiIiOjZwpwpRE3I6NGjsX79egA1S0bc3NzUthswYAD69u2LjIwM5OXl4fXXX8ekSZNgZ2eHyspKJCUl4eDBgwAALy8vxMbGaj3ukCFDkJiYiJycHMyfPx+vvvqqylIZFxcXvSpUGBkZ4euvv8bUqVMhlUoRHByMkydPwtPTE23btsXt27cRGRmJrKwsjBkzBomJiXXuuy5atGiBKVOmYOPGjZBKpfD398ekSZPg5OSEhw8f4ty5c4iOjoZcLoe7uzsOHz6ssS83Nzf4+PggIiICxcXFmDhxIry9vTFgwAA0a9YM6enp2LdvHyQSCTw8PIRyq8rLp/RVUFCAVatWYe3atXB1dUX//v3RpUsXtGzZEsXFxbhw4QLi4+OF4MjUqVO1fj+jR4+GWCzGuHHjMHHiRHTr1g2lpaVITEwUloGJRCJ8+eWXapOQuru7Y9asWQgJCcHt27fx1ltvYdiwYRg6dCisra0hEolQXFyMrKwspKSkICsrC0ZGRvjyyy9V+jEyMsL69esxadIklJSUIDY2FklJSRg7diwcHR2F93fx4kUcOXIEJiYmeOWVV574czSE1atXw9fXF7m5uTh58iTGjBkDT09PODk5wdzcHKWlpbhy5QqOHDmCvLy8J5qdZOjrZeXKlfD29kZRURH279+PjIwMeHt7o2vXrigrK8Phw4dVAlXLly9XyU0C1ATrNmzYgK+++gpDhw6Fk5MTrK2t0aJFCxQVFeHMmTMQi8XCjKgPP/xQ5fU+Pj746aefUFFRgcTERHh7e+ONN96AtbU17t+/j8TERKSkpMDKygoODg5alyRNnDgRsbGxSE5ORk5OjnAe9+nTBzKZDKmpqYiMjAQAletZ3TVobW2N77//HoGBgXj48CE+++wzhIWFwd3dHba2tmjZsiXKyspw48YNpKWl4ezZs5DJZCrJdR+XnJwsBKGVZxkRERHRs43BFKImxMHBAb1798alS5cQFxeHefPmqW0nEomwbt06TJ06FXfu3BGeniozMTHB0qVL0axZM53BlFmzZuGvv/5CZWUl4uPjay1nOXToEGxsbPR6L4MGDcK6devw6aefoqKiAqmpqUhNTVVpM3jwYKxatcrgwRQACAwMRFpaGk6cOIGKigps3bpVZb+5uTm+++47pKenaw2mADUzUsrLy5GQkICqqir89ttv+O2334T9zZo1w2effQYLCwshmKIuKFFXisSsUqkUx48f15j3RiQSwd/fX2dVkgULFkAkEiExMVEI1ikzNjbGsmXLMGrUKI19zJ07F88//zxWr14NiUSidVxAzU2rOnZ2dtizZw/mzJmDzMxMFBQUaFzaou85Vx/atm2LPXv24KOPPkJKSgpKS0trff8K/yahriGvF2tra/z6668IDAxEdnY2bty4gXXr1tVq16pVKyxfvlxtJSjFe6moqBBKIatjbGyMuXPnwtfXV+XvHTp0wNq1a/Hxxx+jqqoKly9frjW7o2PHjtiwYQN+/fVXre9HJBJh48aN+PDDD3HmzBmUlpbWup5NTEywatUqXL16VbieNV2Dw4cPx65du7BgwQLcunVL7diUmZqaqgSXH6eY2dWyZUuVZVZERET0bGMwhaiJ8fPzwxdffIGcnBycPXsWAwcOVNvO1tYWkZGRCAsLg1gsRm5uLoyMjNCxY0cMGzYMkydPhr29vUqFDU169+6NiIgIhIWF4cyZM8jPzzdI/pQxY8YgLi4OW7duxdGjR3H37l2YmZnBzs4O48ePh6+vL5o3r5+foRYtWmDz5s3Yu3cvoqOjcfXqVUilUlhbW2PEiBGYOnUqunTponYJ0OOMjY3x448/4o8//sDvv/+OjIwMSCQStG/fHoMGDcKUKVPg7OyMzZs3C6/5N+VRJ0yYgB49euDkyZM4f/48rl+/joKCAlRVVcHU1BQ2NjYYNGgQ3nzzTfTp06dO49+wYQNiYmIQERGBq1ev4sGDB7CysoKbmxvee++9OuV58PX1hYeHB/bt24fjx48jMzNTWPby3HPPoVu3bujXrx+GDx+u9Ul+t27dEB0dLQTuLly4gKKiIshkMlhaWqJnz55wc3PTWu65IbVv3x47d+7E0aNHsX//fpw9exaFhYWQSqVo3bo1evToAVdX11pLpPRlyOvFzs4OsbGxiIqKQkJCAi5fvoySkhLh/Bk+fDj8/PxUcp4omzFjBlxcXHDq1Cn8/fffyM7ORmFhIR49egQzMzPY2trC1dUVvr6+sLW1VdvH6NGjERkZiZ9//hmnTp3C/fv3YWZmhs6dO2PUqFHw8/ODpaWlzmAKUBP83LlzJyIiIhAdHY3MzEz8888/6NixI9zc3BAQEAB7e3uV5WXarkFnZ2fEx8fjwIEDOHz4sHAOPnz4EObm5rCxsUGfPn3g5uaGV155RWM1IolEIiQnHj9+vF5JfImIiOjpJpIbqnwHERlEVVUVRo4cicLCQrz99ttYsWJFYw+J6mjOnDn4448/ANRM/a+vUrp1sWjRImHpw5PMLCJ6Gvn4+CAjIwMWFhY4ffp0vZffDg8Px5IlSyASiRAXF8fks0RERP8hTEBL1MSYmJhg+vTpAGqSRKpLtkhNT25urvCE2tHRsVEDKUT/RefOnROqc7m4uNR7IEUmk+Hnn38GAHh6ejKQQkRE9B/DYApREzR58mTY2NigqqpKZekINY6cnBzk5+dr3H/37l3Mnj1bKPH69ttvN9TQiP4TMjMzUVxcrHF/VlYWPvnkE+HfDXENxsXFITs7G8bGxhrzWxEREdGzizlTiJogExMTBAUFYdasWfj9998xffp0jbkNqP6dO3cOixcvxuDBgzF48GChsk5JSQnS0tJw8OBBIceMs7MzgylEBiYWixEaGgo3NzcMGDAAnTt3RvPmzVFUVITTp09DLBYLwUwPDw+hpHV9kclk2LRpEwBg2rRpGvPGEBER0bOLwRSiJmr06NFPVGaV6sejR4+QlJSEpKQkjW1cXFwQHBwMIyOjBhwZ0X9DVVUV/vzzT5Wyzo/z9PTE6tWr630sRkZGtaqeERER0X8LgylERDq4u7tj5cqVOHHiBK5fv47i4mKUlJTA2NgY7du3R79+/TB27Fi4u7s39lCJnklvvfUWzMzMkJSUhJs3b6K4uBhlZWUwMTGBlZUVBg4cCB8fHwwePLixh0pERET/EazmQ0RERERERESkByagJSIiIiIiIiLSA4MpRERERERERER6YDCFiIiIiIiIiEgPDKYQEREREREREemBwRQiIiIiIiIiIj0wmEJEREREREREpAcGU4iIiIiIiIiI9MBgChERERERERGRHhhMISIiIiIiIiLSA4MpRERERERERER6YDCFiIiIiIiIiEgP/w9V8M8n3NZxuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "sns.scatterplot(data=scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index(), x='Observed % cell death', y='Predicted % cell death', hue='Patient', style='Drug', s=150, alpha=0.9, ax=ax)\n",
    "\n",
    "for _, row in scatter_single_df.groupby(['Drug', 'Patient']).agg(['min', 'max', 'median']).iterrows():\n",
    "    ax.plot([row[('Observed % cell death', 'min')], row[('Observed % cell death', 'max')]], \n",
    "            [row[('Predicted % cell death', 'median')], row[('Predicted % cell death', 'median')]], \n",
    "            color='grey', zorder=0, alpha=0.5)\n",
    "    \n",
    "\n",
    "vmin = scatter_single_df[['Observed % cell death', 'Predicted % cell death']].min().min()\n",
    "vmax = scatter_single_df[['Observed % cell death', 'Predicted % cell death']].max().max()\n",
    "\n",
    "ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "ax.set_xlim((vmin-5, 100))\n",
    "ax.set_ylim((vmin-5, 100))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0, markerscale=2)\n",
    "\n",
    "x = scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index()['Observed % cell death'].values\n",
    "y = scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index()['Predicted % cell death'].values\n",
    "\n",
    "scor, pval = stats.pearsonr(x, y)\n",
    "print ('Single drug | Pearson r = {:.2f} ({:.2e})'.format(scor, pval))\n",
    "\n",
    "r2 = metrics.r2_score(x, y)\n",
    "print ('Single drug [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "if dosage_used == 'Median IC50':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at drug-specific dosage)')\n",
    "elif dosage_used == '3 fold':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at 3-fold dilution)')\n",
    "else:\n",
    "    ax.set_xlabel('Observed % cell death\\n({})'.format(dosage_used))\n",
    "\n",
    "sns.despine()\n",
    "# plt.tight_layout()\n",
    "\n",
    "# fig.savefig('../figure/Fig4E_single_drug_{}_cell_{}.svg'.format(dosage_used, model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For combi drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:50.866163Z",
     "start_time": "2020-11-17T13:31:50.837320Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 5)\n",
      "(70, 3)\n",
      "(70, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug combination</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % cell death</th>\n",
       "      <th>Predicted % cell death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN120</td>\n",
       "      <td>85.90</td>\n",
       "      <td>99.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN137</td>\n",
       "      <td>94.32</td>\n",
       "      <td>99.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN148</td>\n",
       "      <td>91.27</td>\n",
       "      <td>98.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN159</td>\n",
       "      <td>92.54</td>\n",
       "      <td>97.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN160</td>\n",
       "      <td>65.67</td>\n",
       "      <td>93.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Drug combination                              File name Replicate  \\\n",
       "65  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "66  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "67  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "68  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "69  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "\n",
       "   Patient  Observed % cell death  Predicted % cell death  \n",
       "65   HN120                  85.90                   99.12  \n",
       "66   HN137                  94.32                   99.16  \n",
       "67   HN148                  91.27                   98.89  \n",
       "68   HN159                  92.54                   97.21  \n",
       "69   HN160                  65.67                   93.25  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_df = obs_combi_kill_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug combination', 'File name', 'Replicate', 'Patient', 'Observed % cell death']\n",
    "print (obs_df.shape)\n",
    "\n",
    "pred_df = pred_combi_kill_df.loc[[d[0] for d in obs_combi_kill_df.index], patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug combination', 'Patient', 'Predicted % cell death']\n",
    "print (pred_df.shape)\n",
    "\n",
    "scatter_combi_df = pd.concat([obs_df, pred_df[['Predicted % cell death']]], axis=1)\n",
    "scatter_combi_df.loc[:, 'Replicate'] = scatter_combi_df['Replicate'].astype(str)\n",
    "print (scatter_combi_df.shape)\n",
    "scatter_combi_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:51.328379Z",
     "start_time": "2020-11-17T13:31:50.868237Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drug combination | 0.22 (2.80e-01)\n",
      "Drug combination [R-sq -106.88%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "sns.scatterplot(data=scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index(), x='Observed % cell death', y='Predicted % cell death', hue='Drug combination', style='Patient', s=150, alpha=1.0)\n",
    "\n",
    "for _, row in scatter_combi_df.groupby(['Drug combination', 'Patient']).agg(['min', 'max', 'median']).iterrows():\n",
    "    ax.plot([row[('Observed % cell death', 'min')], row[('Observed % cell death', 'max')]], \n",
    "            [row[('Predicted % cell death', 'median')], row[('Predicted % cell death', 'median')]], \n",
    "            color='grey', zorder=0, alpha=0.5)\n",
    "\n",
    "vmin = scatter_combi_df[['Observed % cell death', 'Predicted % cell death']].min().min()\n",
    "vmax = scatter_combi_df[['Observed % cell death', 'Predicted % cell death']].max().max()\n",
    "\n",
    "ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "ax.set_xlim((vmin-5, 100))\n",
    "ax.set_ylim((vmin-5, 100))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0, markerscale=2)\n",
    "\n",
    "x = scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index()['Observed % cell death'].values\n",
    "y = scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index()['Predicted % cell death'].values\n",
    "\n",
    "scor, pval = stats.pearsonr(x, y)\n",
    "print ('Drug combination | {:.2f} ({:.2e})'.format(scor, pval))\n",
    "\n",
    "r2 = metrics.r2_score(x, y)\n",
    "print ('Drug combination [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "if dosage_used == 'Median IC50':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at drug-specific dosage)')\n",
    "if dosage_used == '3 fold':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at 3-fold dilution)')\n",
    "else:\n",
    "    ax.set_xlabel('Observed % cell death\\n({})'.format(dosage_used))\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "# fig.savefig('../figure/Fig4_combi_drug_{}_cell_{}.svg'.format(dosage_used, model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare improvement of drug combi over single drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:51.344959Z",
     "start_time": "2020-11-17T13:31:51.329993Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_combi_imp_df = pred_combi_df[['patient', 'Combi Name', 'improve']].pivot(index='Combi Name', columns='patient', values='improve')\n",
    "pred_combi_pimp_df = pred_combi_df[['patient', 'Combi Name', 'improve_p']].pivot(index='Combi Name', columns='patient', values='improve_p')\n",
    "\n",
    "pred_combi_imp_df = pred_combi_imp_df.loc[combi_drug_list, patient_list]\n",
    "pred_combi_pimp_df = pred_combi_pimp_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:51.355919Z",
     "start_time": "2020-11-17T13:31:51.346596Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>patient</th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Combi Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Epothilone B</th>\n",
       "      <td>13.15</td>\n",
       "      <td>10.23</td>\n",
       "      <td>15.45</td>\n",
       "      <td>11.69</td>\n",
       "      <td>8.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Gefitinib</th>\n",
       "      <td>14.43</td>\n",
       "      <td>13.88</td>\n",
       "      <td>23.66</td>\n",
       "      <td>19.93</td>\n",
       "      <td>20.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gefitinib|Epothilone B</th>\n",
       "      <td>7.73</td>\n",
       "      <td>5.71</td>\n",
       "      <td>14.75</td>\n",
       "      <td>9.90</td>\n",
       "      <td>9.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B|PI-103</th>\n",
       "      <td>2.93</td>\n",
       "      <td>3.54</td>\n",
       "      <td>2.81</td>\n",
       "      <td>6.24</td>\n",
       "      <td>13.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doxorubicin|Vorinostat</th>\n",
       "      <td>8.86</td>\n",
       "      <td>9.75</td>\n",
       "      <td>10.49</td>\n",
       "      <td>13.40</td>\n",
       "      <td>10.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "patient                 HN120  HN137  HN148  HN159  HN160\n",
       "Combi Name                                               \n",
       "Docetaxel|Epothilone B  13.15  10.23  15.45  11.69   8.09\n",
       "Docetaxel|Gefitinib     14.43  13.88  23.66  19.93  20.51\n",
       "Gefitinib|Epothilone B   7.73   5.71  14.75   9.90   9.86\n",
       "Epothilone B|PI-103      2.93   3.54   2.81   6.24  13.20\n",
       "Doxorubicin|Vorinostat   8.86   9.75  10.49  13.40  10.83"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_combi_imp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:51.376232Z",
     "start_time": "2020-11-17T13:31:51.358149Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Epothilone B</th>\n",
       "      <td>64.20</td>\n",
       "      <td>71.45</td>\n",
       "      <td>50.30</td>\n",
       "      <td>52.19</td>\n",
       "      <td>37.40</td>\n",
       "      <td>84.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Gefitinib</th>\n",
       "      <td>69.45</td>\n",
       "      <td>70.71</td>\n",
       "      <td>68.32</td>\n",
       "      <td>78.14</td>\n",
       "      <td>23.30</td>\n",
       "      <td>65.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doxorubicin|Vorinostat</th>\n",
       "      <td>78.23</td>\n",
       "      <td>77.29</td>\n",
       "      <td>84.78</td>\n",
       "      <td>45.46</td>\n",
       "      <td>30.06</td>\n",
       "      <td>65.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B|PI-103</th>\n",
       "      <td>86.18</td>\n",
       "      <td>94.34</td>\n",
       "      <td>90.95</td>\n",
       "      <td>92.48</td>\n",
       "      <td>66.34</td>\n",
       "      <td>83.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gefitinib|Epothilone B</th>\n",
       "      <td>74.99</td>\n",
       "      <td>78.81</td>\n",
       "      <td>76.18</td>\n",
       "      <td>81.57</td>\n",
       "      <td>35.26</td>\n",
       "      <td>88.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        HN120  HN137  HN148  HN159  HN160  HN182\n",
       "Drug                                                            \n",
       "Docetaxel|Epothilone B  64.20  71.45  50.30  52.19  37.40  84.13\n",
       "Docetaxel|Gefitinib     69.45  70.71  68.32  78.14  23.30  65.20\n",
       "Doxorubicin|Vorinostat  78.23  77.29  84.78  45.46  30.06  65.83\n",
       "Epothilone B|PI-103     86.18  94.34  90.95  92.48  66.34  83.01\n",
       "Gefitinib|Epothilone B  74.99  78.81  76.18  81.57  35.26  88.09"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TEMPORARY: using averge response\n",
    "\n",
    "temp_df = obs_kill_df.loc[combi_drug_list].reset_index().drop(['File name', 'Replicate'], axis=1)\n",
    "temp_df = temp_df.groupby('Drug').mean()\n",
    "# temp_df = temp_df.loc[~temp_df.index.duplicated(keep='first')]\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:51.403702Z",
     "start_time": "2020-11-17T13:31:51.378181Z"
    }
   },
   "outputs": [],
   "source": [
    "imp_results = []\n",
    "pimp_results = []\n",
    "for c, data in temp_df.loc[combi_drug_list, patient_list].iterrows():\n",
    "    a, b = c.split('|')\n",
    "    \n",
    "    c_kill = data.values\n",
    "    best_kill = obs_kill_df.loc[[a, b]].max()[patient_list]\n",
    "#     best_kill = obs_kill_df.loc[[a, b]].reset_index().groupby('Drug').mean()[patient_list].max()\n",
    "    \n",
    "#     print (c, c_kill, best_kill)\n",
    "    \n",
    "    imp = list((c_kill - best_kill).values)\n",
    "    pimp = list(((c_kill - best_kill)/best_kill).values)\n",
    "    \n",
    "    imp_results += [[c] + imp]\n",
    "    pimp_results += [[c] + pimp]\n",
    "        \n",
    "obs_combi_imp_df = pd.DataFrame(imp_results)\n",
    "obs_combi_imp_df.columns = ['Drug combination'] + patient_list\n",
    "obs_combi_imp_df = obs_combi_imp_df.set_index('Drug combination')\n",
    "\n",
    "obs_combi_pimp_df = pd.DataFrame(pimp_results)\n",
    "obs_combi_pimp_df.columns = ['Drug combination'] + patient_list\n",
    "obs_combi_pimp_df = obs_combi_pimp_df.set_index('Drug combination')\n",
    "\n",
    "obs_combi_imp_df = obs_combi_imp_df.loc[combi_drug_list, patient_list]\n",
    "obs_combi_pimp_df = obs_combi_pimp_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:51.789732Z",
     "start_time": "2020-11-17T13:31:51.406340Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs_df = obs_combi_imp_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug combination', 'Patient', 'Observed % death increase']\n",
    "\n",
    "pred_df = pred_combi_imp_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug combination', 'Patient', 'Predicted % death increase']\n",
    "\n",
    "sns.set(font_scale=1.25, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "scatter_df = pd.merge(obs_df, pred_df, left_on=['Drug combination', 'Patient'], right_on=['Drug combination', 'Patient'])\n",
    "\n",
    "sns.scatterplot(data=scatter_df, x='Observed % death increase', y='Predicted % death increase', hue='Drug combination', style='Patient', s=100, alpha=0.7)\n",
    "# sns.regplot(data=scatter_df, x='Observed % death increase', y='Predicted % death increase', x_ci='ci', ci=99, scatter=False, color='grey')\n",
    "# plt.plot([0, 25], [0, 25], ls=\"--\", c=\".3\")\n",
    "\n",
    "vmin = scatter_df[['Observed % death increase', 'Predicted % death increase']].min().min()\n",
    "vmax = scatter_df[['Observed % death increase', 'Predicted % death increase']].max().max()\n",
    "\n",
    "# ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "# ax.set_xlim((vmin-5, vmax+5))\n",
    "# ax.set_ylim((vmin-5, vmax+5))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0)\n",
    "\n",
    "scor, pval = stats.pearsonr(obs_df['Observed % death increase'].values, pred_df['Predicted % death increase'].values)\n",
    "ax.set_title('Single VS Combi\\n(Pearson r = {:.2f} p-val < {:.2e})'.format(scor, pval))\n",
    "\n",
    "# r2 = metrics.r2_score(scatter_df['Observed % death increase'].values, scatter_df['Predicted % death increase'].values)\n",
    "# ax.set_title('Single VS Combi [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "plt.tight_layout()\n",
    "# fig.savefig('../figure/Fig4_improvement_{}_scatter_cell.svg'.format(dosage_used))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:51.803956Z",
     "start_time": "2020-11-17T13:31:51.791431Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug combination</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % death increase</th>\n",
       "      <th>Predicted % death increase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN148</td>\n",
       "      <td>11.62</td>\n",
       "      <td>23.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN160</td>\n",
       "      <td>-3.63</td>\n",
       "      <td>20.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN159</td>\n",
       "      <td>11.57</td>\n",
       "      <td>19.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN148</td>\n",
       "      <td>-13.34</td>\n",
       "      <td>15.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN148</td>\n",
       "      <td>12.54</td>\n",
       "      <td>14.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN120</td>\n",
       "      <td>10.50</td>\n",
       "      <td>14.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN137</td>\n",
       "      <td>7.34</td>\n",
       "      <td>13.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN159</td>\n",
       "      <td>11.36</td>\n",
       "      <td>13.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN160</td>\n",
       "      <td>7.39</td>\n",
       "      <td>13.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN120</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>13.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN159</td>\n",
       "      <td>-7.97</td>\n",
       "      <td>11.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN160</td>\n",
       "      <td>5.86</td>\n",
       "      <td>10.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN148</td>\n",
       "      <td>30.50</td>\n",
       "      <td>10.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN137</td>\n",
       "      <td>-6.06</td>\n",
       "      <td>10.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN159</td>\n",
       "      <td>14.99</td>\n",
       "      <td>9.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN160</td>\n",
       "      <td>2.45</td>\n",
       "      <td>9.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN137</td>\n",
       "      <td>14.13</td>\n",
       "      <td>9.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN120</td>\n",
       "      <td>10.70</td>\n",
       "      <td>8.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN160</td>\n",
       "      <td>4.60</td>\n",
       "      <td>8.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN120</td>\n",
       "      <td>9.37</td>\n",
       "      <td>7.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN159</td>\n",
       "      <td>6.03</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN137</td>\n",
       "      <td>1.30</td>\n",
       "      <td>5.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN137</td>\n",
       "      <td>15.30</td>\n",
       "      <td>3.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN120</td>\n",
       "      <td>9.76</td>\n",
       "      <td>2.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN148</td>\n",
       "      <td>5.97</td>\n",
       "      <td>2.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Drug combination Patient  Observed % death increase  \\\n",
       "7      Docetaxel|Gefitinib   HN148                      11.62   \n",
       "9      Docetaxel|Gefitinib   HN160                      -3.63   \n",
       "8      Docetaxel|Gefitinib   HN159                      11.57   \n",
       "2   Docetaxel|Epothilone B   HN148                     -13.34   \n",
       "12  Gefitinib|Epothilone B   HN148                      12.54   \n",
       "5      Docetaxel|Gefitinib   HN120                      10.50   \n",
       "6      Docetaxel|Gefitinib   HN137                       7.34   \n",
       "23  Doxorubicin|Vorinostat   HN159                      11.36   \n",
       "19     Epothilone B|PI-103   HN160                       7.39   \n",
       "0   Docetaxel|Epothilone B   HN120                      -1.41   \n",
       "3   Docetaxel|Epothilone B   HN159                      -7.97   \n",
       "24  Doxorubicin|Vorinostat   HN160                       5.86   \n",
       "22  Doxorubicin|Vorinostat   HN148                      30.50   \n",
       "1   Docetaxel|Epothilone B   HN137                      -6.06   \n",
       "13  Gefitinib|Epothilone B   HN159                      14.99   \n",
       "14  Gefitinib|Epothilone B   HN160                       2.45   \n",
       "21  Doxorubicin|Vorinostat   HN137                      14.13   \n",
       "20  Doxorubicin|Vorinostat   HN120                      10.70   \n",
       "4   Docetaxel|Epothilone B   HN160                       4.60   \n",
       "10  Gefitinib|Epothilone B   HN120                       9.37   \n",
       "18     Epothilone B|PI-103   HN159                       6.03   \n",
       "11  Gefitinib|Epothilone B   HN137                       1.30   \n",
       "16     Epothilone B|PI-103   HN137                      15.30   \n",
       "15     Epothilone B|PI-103   HN120                       9.76   \n",
       "17     Epothilone B|PI-103   HN148                       5.97   \n",
       "\n",
       "    Predicted % death increase  \n",
       "7                        23.66  \n",
       "9                        20.51  \n",
       "8                        19.93  \n",
       "2                        15.45  \n",
       "12                       14.75  \n",
       "5                        14.43  \n",
       "6                        13.88  \n",
       "23                       13.40  \n",
       "19                       13.20  \n",
       "0                        13.15  \n",
       "3                        11.69  \n",
       "24                       10.83  \n",
       "22                       10.49  \n",
       "1                        10.23  \n",
       "13                        9.90  \n",
       "14                        9.86  \n",
       "21                        9.75  \n",
       "20                        8.86  \n",
       "4                         8.09  \n",
       "10                        7.73  \n",
       "18                        6.24  \n",
       "11                        5.71  \n",
       "16                        3.54  \n",
       "15                        2.93  \n",
       "17                        2.81  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter_df.sort_values('Predicted % death increase', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:51.811687Z",
     "start_time": "2020-11-17T13:31:51.806057Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def change_boxplot_edge_color(ax, col):\n",
    "    for i, artist in enumerate(ax.artists):\n",
    "        # Set the linecolor on the artist to the facecolor, and set the facecolor to None\n",
    "        artist.set_edgecolor(col)\n",
    "        # artist.set_facecolor('None')\n",
    "\n",
    "        # Each box has 6 associated Line2D objects (to make the whiskers, fliers, etc.)\n",
    "        # Loop over them here, and use the same colour as above\n",
    "        for j in range(i*6,i*6+6):\n",
    "            line = ax.lines[j]\n",
    "            line.set_color(col)\n",
    "            line.set_mfc(col)\n",
    "            line.set_mec(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:51.817005Z",
     "start_time": "2020-11-17T13:31:51.813674Z"
    }
   },
   "outputs": [],
   "source": [
    "cutoff = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:52.014854Z",
     "start_time": "2020-11-17T13:31:51.819068Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RanksumsResult(statistic=-0.3495310368212778, pvalue=0.726690674333039) Ttest_indResult(statistic=-0.38044374156503413, pvalue=0.7071070050128384)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(3, 4))\n",
    "\n",
    "merge_df = pd.merge(obs_df, pred_df, left_on=['Drug combination', 'Patient'], right_on=['Drug combination', 'Patient'])\n",
    "merge_df.loc[:, 'Observed increase'] = merge_df['Observed % death increase'] >= cutoff\n",
    "\n",
    "sns.boxplot(data=merge_df, x='Observed increase', y='Predicted % death increase', fliersize=4, color='lightblue', ax=ax)\n",
    "# sns.swarmplot(data=merge_df, x='Observed increase', y='Predicted % death increase', color='black', alpha=0.5, ax=ax)\n",
    "change_boxplot_edge_color(ax, 'black')\n",
    "ax.set_xlabel('Observed increase ' + r'$\\geq {}\\%$'.format(cutoff))\n",
    "\n",
    "x = merge_df.loc[merge_df['Observed increase'], 'Predicted % death increase'].values\n",
    "y = merge_df.loc[~merge_df['Observed increase'], 'Predicted % death increase'].values\n",
    "\n",
    "sns.despine()\n",
    "print (stats.ranksums(x, y), stats.ttest_ind(x, y))\n",
    "plt.tight_layout()\n",
    "\n",
    "# fig.savefig('../figure/Fig4_improvement_{}_{}_cell.svg'.format(dosage_used, cutoff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:52.022413Z",
     "start_time": "2020-11-17T13:31:52.017629Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.35 (7.27e-01)\n"
     ]
    }
   ],
   "source": [
    "print (\"{:.2f} ({:.2e})\".format(*stats.ranksums(x, y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:52.033821Z",
     "start_time": "2020-11-17T13:31:52.024477Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 8, 1: 17})"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs = (merge_df['Observed % death increase'].values >= cutoff).astype(int)\n",
    "pred = (merge_df['Predicted % death increase'].values >= cutoff).astype(int)\n",
    "pred_val = merge_df['Predicted % death increase'].values\n",
    "\n",
    "Counter(obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:52.043391Z",
     "start_time": "2020-11-17T13:31:52.035266Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.56, 0.717948717948718, 0.0)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(obs, pred), metrics.f1_score(obs, pred, pos_label=1), metrics.f1_score(obs, pred, pos_label=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:31:52.240966Z",
     "start_time": "2020-11-17T13:31:52.045231Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6769397765124171\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.1)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision, recall, thresholds = metrics.precision_recall_curve(obs, pred_val)\n",
    "plt.plot(recall, precision)\n",
    "\n",
    "print (metrics.auc(recall, precision))\n",
    "\n",
    "no_skill = len(obs[obs==1]) / len(obs)\n",
    "plt.axhline(y=no_skill)\n",
    "\n",
    "plt.xlim((0,1))\n",
    "plt.ylim((0,1.1))\n",
    "\n",
    "# fig.savefig('../figure/Fig4_pr_curve_{}_{}.svg'.format(dosage_used, cutoff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
